CN118338839A

CN118338839A - Techniques for measuring heart rate during exercise

Info

Publication number: CN118338839A
Application number: CN202280079544.4A
Authority: CN
Inventors: 张玺; R·李; A·A·兰塔宁; T·J·瓦柳斯; J·P·耶尔韦勒; G·冯
Original assignee: Euler Health Co
Current assignee: Euler Health Co
Priority date: 2021-10-12
Filing date: 2022-09-29
Publication date: 2024-07-12

Abstract

Methods, systems, and devices for heart rate detection are described. A method for measuring a heart rate of a user may include: physiological data associated with a user is received, wherein the physiological data may include photoplethysmography (PPG) data and motion data collected over a first time interval via a wearable device associated with the user. The method may include: determining a set of candidate heart rate measurements within a first time interval based at least in part on PPG data; selecting a first heart rate measurement from the set of candidate heart rate measurements based on the received motion data; and determining a first heart rate of the user over a first time interval based on the selected first heart rate measurement.

Description

Techniques for measuring heart rate during exercise

Cross reference

This patent application claims the benefit of U.S. non-provisional patent application 17/954,564 entitled "TECHNIQUES FOR MEASURING HEART RATE DURING EXERCISE (a technique for measuring heart rate during exercise)" filed by ZHANG et al at 9, 28, 2022, which claims the benefit of U.S. provisional patent application 63/254,849 entitled "TECHNIQUES FOR MEASURING HEART RATE DURING EXERCISE (a technique for measuring heart rate during exercise)" filed by ZHANG et al at 10, 12, 2021, which is assigned to the present assignee and expressly incorporated herein by reference.

Technical Field

The following relates to wearable devices and data processing, including techniques for measuring heart rate during exercise.

Background

Some wearable devices may be configured to collect data associated with a user's heart rate from the user, such as athletic data, temperature data, photoplethysmogram (PPG) data, and the like. In some cases, some wearable devices may not be able to accurately determine heart rate data of the user, such as when the user is exercising or otherwise moving.

Drawings

Fig. 1 illustrates an example of a system supporting techniques for measuring heart rate in accordance with various aspects of the present disclosure.

Fig. 2 illustrates an example of a system supporting techniques for measuring heart rate in accordance with various aspects of the present disclosure.

Fig. 3 illustrates an example of a heart rate determination procedure supporting techniques for measuring heart rate in accordance with aspects of the present disclosure.

Fig. 4 illustrates an example of a heart rate determination procedure supporting techniques for measuring heart rate in accordance with aspects of the present disclosure.

Fig. 5 illustrates an example of a Graphical User Interface (GUI) supporting techniques for measuring heart rate in accordance with various aspects of the disclosure.

Fig. 6 illustrates a block diagram of an apparatus supporting techniques for measuring heart rate in accordance with various aspects of the disclosure.

Fig. 7 illustrates a block diagram of a wearable application supporting techniques for measuring heart rate in accordance with various aspects of the disclosure.

Fig. 8 illustrates a diagram of a system including a device supporting techniques for measuring heart rate in accordance with various aspects of the present disclosure.

Fig. 9-11 illustrate flowcharts of methods supporting techniques for measuring heart rate in accordance with various aspects of the present disclosure.

Detailed Description

The user may use a device (e.g., a wearable device) to determine a physiological measurement of the user, such as heart rate. Some wearable devices may use photo-capacitive pulse wave trace (PPG) data to determine a user's heart rate over time. For example, the wearable device may measure the heart rate of the user by detecting a change in blood pulse volume through a PPG sensor (e.g., an infrared PPG sensor, an infrared Light Emitting Diode (LED)) in the wearable device. Each time the user's heart beats, blood is pumped out to arteries located in the user's hand and fingers. PPG sensors are able to detect these changes in blood flow and blood volume using light reflection and absorption. Each pulse causes an artery in the user's finger to alternate between inflation and deflation. By shining light on the skin of the user, in particular on the skin of the finger, the change in light absorbed by the blood and reflected back from the wave volume of the erythrocytes in the artery is measured. From here, the PPG may represent these blood flow changes by visual waveforms representing the activity of the user's heart (e.g., heart rate).

Heart rate is a sensitive metric that is subject to change based on the activity the user is engaged in (e.g., drinking a cup of water, standing, watching television, exercising). Some activities may result in spikes or pits (dip) in heart rate, and such changes may be referred to as "noise" in the data. Furthermore, some activities may generate PPG signals that may be misinterpreted as heart rate signals. For example, motion such as running, jumping, etc. may result in motion artifacts (e.g., false PPG signals). The optical properties of the PPG sensor may make PPG measurements susceptible to motion artifacts from variable and discontinuous contact between the device and the skin. Motion artifacts are typically caused by changes in blood flow velocity caused by exercise motion or relative motion between the PPG sensor and the user's skin. For example, some movements (e.g., running) may result in periodic pressure between the wearable device (e.g., a sensor of the wearable device) and the portion where the user's wearable device is located, and thus may apply periodic pressure to the blood vessel. The periodic pressure caused by the periodic motion may cause the blood vessel to periodically contract and expand. Thus, the wearable device may detect such contraction and expansion of the blood vessel as an artificial PPG signal, as the contraction and expansion is due to the user's motion and not due to the user's heart rate. Such artificial PPG signals attributable to motion may be referred to as "motion artifacts.

As such, due to these difficulties in measuring heart rate during exercise or other exercise, some conventional wearable devices may not accurately track the user's heart rate throughout the day and/or during the exercise session, or may not track the user's heart rate throughout the day and/or during the exercise session at all. In such a case, the wearable device may provide only a limited depiction of the heart rate of the user, and thus the overall health of the user. Additionally, heart rate may be a valuable tool for the user during exercise, as heart rate may provide an indication of effort and strength during exercise. For example, the user may utilize the heart rate during an exercise to determine whether to increase or decrease the intensity of the exercise. Thus, it may be beneficial to implement techniques that accurately determine a user's heart rate during an activity period, such as during exercise. The wearable devices described herein may be configured with a program for detecting cardiac data and distinguishing actual heart rate data of a user from motion artifacts affecting the data.

Accordingly, various aspects of the present disclosure relate to techniques for measuring a user's heart rate in a manner that is less susceptible to motion and other noise. A process for determining heart rate may include: physiological data associated with the user is received, wherein the physiological data may include PPG data and motion data (e.g., acceleration data) acquired throughout a first time interval via a wearable device associated with the user. In some cases, the motion data may refer to acceleration data and may be used to determine a period of motion of the user. The method may include: a set of candidate heart rate measurements within a first time interval is determined based at least in part on PPG data. The set of candidate heart rate measurements may include artificial heart rate measurements (e.g., heart rate measurements attributable to periodic motion or other activity). Thus, the method may comprise: a first heart rate measurement is selected from the set of candidate heart rate measurements based on the received motion data. For example, the set of candidate heart rate measurements may be compared to the motion data to determine candidate heart rate measurements related to the motion. Accordingly, heart rate data not attributable to exercise may be selected. The method may be used to determine a first heart rate of the user over a first time interval based on the selected first heart rate measurement.

In some implementations, a machine learning model or algorithm (e.g., heuristic-based model, deep learning model, regression-based model) may be used to determine heart rate measurements of the user. For example, the wearable device may acquire PPG data and motion data associated with the user. The acquired PPG data and motion data may be input into a machine learning model configured to output a determined or estimated heart rate for the user. For example, the machine learning model may be configured to distinguish between candidate heart rate measurements attributable to motion artifacts and candidate heart rate measurements indicative of an actual heart rate of the user. In such cases, the machine learning model may be configured to select (e.g., identify, estimate) candidate heart rate measurements, and determine a heart rate of the user based on the selected/estimated candidate heart rate measurements. As another example, the machine learning model may be configured to identify time and/or frequency domain features within the received PPG data and motion data (which may be used to identify candidate heart rate measurements), and may be configured to determine or estimate a heart rate of the user based on the identified features.

The processes described herein may not be limited to determining heart rate during exercise. In some cases, the processes described herein may be used to detect heart rate at all times, or regardless of activity level, so that the wearable device may utilize the described techniques during rest periods, activities, exercise, sleep, and the like. Thus, particular aspects of the subject matter described herein may be implemented to realize one or more advantages. The described techniques may support improvements in detecting and determining heart rate data of a user to provide the user with integrated heart rate data during periods of rest, activity, exercise, sleep.

Aspects of the present disclosure are initially described in the context of a system that supports the collection of physiological data from a user via a wearable device. Additional aspects of the present disclosure are additionally described in the context of an example heart rate determination program and an example Graphical User Interface (GUI). Various aspects of the present disclosure are further illustrated by and described with reference to apparatus, system, and flow diagrams relating to techniques for measuring heart rate during exercise.

Fig. 1 illustrates an example of a system 100 supporting techniques for measuring heart rate in accordance with various aspects of the present disclosure. The system 100 includes a plurality of electronic devices (e.g., wearable device 104, user device 106) that can be worn and/or operated by one or more users 102. The system 100 further includes a network 108 and one or more servers 110.

The electronic devices may include any electronic device known in the art, including wearable devices 104 (e.g., ring wearable devices, watch wearable devices, etc.), user devices 106 (e.g., smart phones, laptops, tablet computers). The electronic devices associated with the respective users 102 may include one or more of the following functions: 1) Measuring physiological data; 2) Storing the measured data; 3) Processing the data; 4) Providing an output (e.g., via a GUI) to the user 102 based on the processed data; and 5) communicate data with each other and/or with other computing devices. Different electronic devices may perform one or more of these functions.

Example wearable devices 104 may include wearable computing devices, such as ring computing devices (hereinafter "rings") configured to be worn on fingers of user 102, wrist computing devices (e.g., smartwatches, exercise bands, or bracelets) configured to be worn on wrists of user 102, and/or head-mounted computing devices (e.g., eyeglasses/goggles). The wearable device 104 may also include a cord, strap (e.g., flexible or inflexible cord or strap), hook and loop sensor, etc., that may be positioned in other locations, such as a strap around the head (e.g., forehead strap), arms (e.g., forearm strap and/or dual-headed strap), and/or legs (e.g., thigh or calf strap), behind the ear, under the armpit, etc. The wearable device 104 may also be attached to or included in an article of apparel. For example, the wearable device 104 may be included in a pocket and/or pouch on the garment. As another example, the wearable device 104 may be clipped and/or pinned to clothing or may otherwise remain in proximity to the user 102. Exemplary articles of apparel may include, but are not limited to, hats, shirts, gloves, pants, socks, jackets (e.g., jackets), and undergarments. In some implementations, the wearable device 104 may be included in other types of devices, such as training/sports devices used during physical activity. For example, the wearable device 104 may be attached to or included in a bicycle, a snowboard, a tennis racket, a golf club, and/or a training weight.

Many of the contents of the present disclosure may be described in the context of a ring wearable device 104. Thus, unless otherwise indicated herein, the terms "ring 104," "wearable device 104," and similar terms may be used interchangeably. However, use of the term "ring 104" should not be considered limiting, as it is contemplated herein that aspects of the present disclosure may be performed using other wearable devices (e.g., a watch wearable device, a necklace wearable device, a bracelet wearable device, an earring wearable device, an ankle wearable device, etc.).

In some aspects, the user device 106 may include a handheld mobile computing device, such as a smartphone and a tablet computing device. User device 106 may also include personal computers, such as laptop and desktop computing devices. Other example user devices 106 may include server computing devices that may communicate with other electronic devices (e.g., via the internet). In some implementations, the computing device may include a medical device, such as an external wearable computing device (e.g., a Holter monitor). The medical devices may also include implantable medical devices such as pacemakers and cardioverter defibrillators. Other example user devices 106 may include home computing devices such as internet of things (IoT) devices (e.g., ioT devices), smart televisions, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and exercise equipment.

Some electronic devices (e.g., wearable device 104, user device 106) may measure physiological parameters of the respective user 102, such as photoplethysmography waveforms, continuous skin temperature, pulse waveforms, respiration rate, heart Rate Variability (HRV), body movement monitoring, galvanic skin response, pulse oximetry, and/or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some/all of the calculations described herein. Some electronic devices may not measure physiological parameters, but may perform some/all of the calculations described herein. For example, a ring (e.g., wearable device 104), mobile device application, or server computing device may process received physiological data measured by other devices.

In some implementations, the user 102 can operate or be associated with a plurality of electronic devices, some of which can measure physiological parameters, and some of which can process the measured physiological parameters. In some implementations, the user 102 can have a ring (e.g., wearable device 104) that measures a physiological parameter. The user 102 may also have a user device 106 (e.g., mobile device, smartphone) or be associated with the user device 106, with the wearable device 104 and the user device 106 communicatively coupled to each other. In some cases, user device 106 may receive data from wearable device 104 and perform some/all of the calculations described herein. In some implementations, the user device 106 may also measure physiological parameters described herein, such as movement/activity parameters.

For example, as shown in fig. 1, a first user 102-a (user 1) may operate, or may be associated with, a wearable device 104-a (e.g., finger ring 104-a) and a user device 106-a that may operate as described herein. In this example, the user device 106-a associated with the user 102-a can process/store the physiological parameter measured by the ring 104-a. In contrast, a second user 102-b (user 2) may be associated with the ring 104-b, the watch wearable device 104-c (e.g., the watch 104-c), and the user device 106-b, wherein the user device 106-b associated with the user 102-b may process/store physiological parameters measured by the ring 104-b and/or the watch 104-c. Further, the nth user 102-N (user N) may be associated with an arrangement of electronic devices (e.g., finger ring 104-N, user device 106-N) described herein. In some aspects, the wearable device 104 (e.g., the ring 104, the watch 104) and other electronic devices may be communicatively coupled to the user devices 106 of the respective users 102 via bluetooth, wi-Fi, and other wireless protocols.

In some implementations, the finger ring 104 (e.g., wearable device 104) of the system 100 can be configured to collect physiological data from the respective user 102 based on arterial blood flow within the user's finger. In particular, the finger ring 104 may utilize one or more LEDs (e.g., red LEDs, green LEDs) that emit light on the palm side of the user's finger to collect physiological data based on arterial blood flow within the user's finger. In some implementations, the finger ring 104 may use a combination of both green and red LEDs to obtain physiological data. The physiological data may include any physiological data known in the art including, but not limited to, temperature data, accelerometer data (e.g., movement/motion data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

The use of both green and red LEDs may provide several advantages over other solutions, as it has been found that red and green LEDs have their own distinct advantages when acquiring physiological data under different conditions (e.g., light/dark, active/inactive), and via different parts of the body, etc. For example, green LEDs have been found to exhibit better performance during exercise. Furthermore, it has been found that a wearable device using multiple LEDs (e.g., green and red LEDs) distributed around the finger ring 104 exhibits superior performance compared to a wearable device using LEDs that are positioned close to each other, such as within a watch wearable device. Furthermore, blood vessels in the finger (e.g., arteries, capillaries) are more accessible via the LED than blood vessels in the wrist. In particular, the arteries in the wrist are located at the bottom of the wrist (e.g., the palm side of the wrist), which means that only capillaries are accessible at the top of the wrist (e.g., the back of the hand side of the wrist), on top of which wearable wrist-watch devices and similar devices are typically worn. As such, it has been found that utilizing LEDs and other sensors within the finger ring 104 exhibits superior performance compared to wearable devices worn on the wrist, as the finger ring 104 can access the artery (as compared to capillaries) more, resulting in stronger signals and more valuable physiological data.

The electronic devices of the system 100 (e.g., user device 106, wearable device 104) may be communicatively coupled to one or more servers 110 via a wired or wireless communication protocol. For example, as shown in fig. 1, an electronic device (e.g., user device 106) may be communicatively coupled to one or more servers 110 via a network 108. The network 108 may implement a transmission control protocol and an internet protocol (TCP/IP) such as the internet, or may implement other network 108 protocols. The network connection between the network 108 and the respective electronic devices may facilitate data transmission via email, network, text message, mail, or any other suitable form of interaction within the computer network 108. For example, in some implementations, a finger ring 104-a associated with a first user 102-a is communicatively coupled to a user device 106-a, where the user device 106-a is communicatively coupled to a server 110 via a network 108. In additional or alternative cases, the wearable device 104 (e.g., ring 104, watch 104) may be directly communicatively coupled to the network 108.

The system 100 may provide on-demand database services between the user device 106 and one or more servers 110. In some cases, server 110 may receive data from user device 106 via network 108 and may store and analyze the data. Similarly, the server 110 may provide data to the user device 106 via the network 108. In some cases, server 110 may be located at one or more data centers. The server 110 may be used for data storage, management, and processing. In some implementations, the server 110 may provide the web-based interface to the user device 106 via a web browser.

In some aspects, the system 100 may detect a period of time that the user 102 is asleep and classify the period of time that the user 102 is asleep as one or more sleep stages (e.g., sleep stage classification). For example, as shown in FIG. 1, a user 102-a may be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a. In this example, the ring 104-a may collect physiological data associated with the user 102-a, including temperature, heart rate, HRV, respiratory rate, and the like. In some aspects, the data collected by the finger ring 104-a may be input to a machine learning classifier, where the machine learning classifier is configured to determine a period of time that the user 102-a is sleeping (or previously sleeping). Further, the machine learning classifier may be configured to classify time periods into different sleep stages, including awake sleep stages, fast eye movement (REM) sleep stages, light sleep stages (non-REM (NREM)), and deep sleep stages (NREM). In some aspects, the categorized sleep stages may be displayed to the user 102-a via a GUI of the user device 106-a. The sleep stage classification may be used to provide feedback to the user 102-a regarding the user's sleep pattern, such as a recommended sleep time, a recommended wake time, etc. Furthermore, in some implementations, the sleep stage classification techniques described herein may be used to calculate scores, such as sleep scores, readiness scores, etc., for respective users.

In some aspects, the system 100 may utilize features derived from circadian rhythms to further improve physiological data collection, data processing procedures, and other techniques described herein. The term circadian rhythm may refer to the natural internal process of regulating the sleep-wake cycle of an individual that repeats approximately every 24 hours. In this regard, the techniques described herein may utilize circadian rhythm adjustment models to improve physiological data collection, analysis, and data processing. For example, the circadian rhythm adjustment model may be input into a machine learning classifier via physiological data collected by the wearable device 104-a from the user 102-a. In this example, the circadian rhythm adjustment model may be configured to "weight" or adjust physiological data collected throughout the user's natural, approximately 24 hours of circadian rhythm. In some implementations, the system may initially begin with a "baseline" circadian rhythm adjustment model, and may modify the baseline model using physiological data collected from each user 102 to generate a customized, personalized circadian rhythm adjustment model specific to each respective user 102.

In some aspects, the system 100 may utilize other biological rhythms to further improve physiological data collection, analysis, and processing through the stages of these other rhythms. For example, if a weekly rhythm is detected within the baseline data of an individual, the model may be configured to adjust the "weights" of the data by day of the week. The biorhythms that may require adjustment of the model by this method include: 1) Overdriving (ultradian) (faster than the circadian rhythm, including sleep cycles in the sleep state, and oscillations from less than an hour period to several hours periods in physiological variables measured during the awake state); 2) Circadian rhythms; 3) Showing a non-endogenous daily rhythm imposed on top of a circadian rhythm, as in a work schedule; 4) A weekly rhythm, or other artificial time period of exogenous application (e.g., a 12 day rhythm may be used in a hypothetical culture with a "week" of 12 days); 5) A female's multi-day ovarian rhythm and a male's spermatogenic rhythm; 6) Lunar rhythms (associated with individuals living with low or no artificial light); and 7) seasonal rhythms.

Biological rhythms are not always resting rhythms. For example, many women experience variability in ovarian cycle length from cycle to cycle, and even within a user, it is not desirable for the superday rhythm to occur at exactly the same time or cycle over several days. As such, signal processing techniques sufficient to quantify the frequency components while maintaining the temporal resolution of these rhythms in the physiological data may be used to improve the detection of these rhythms, assign phases of each rhythm to each moment measured, and thereby modify the comparison of the adjustment model and the time interval. The biorhythmic modulation model and parameters may be added in linear or nonlinear combinations where appropriate to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

In some aspects, the respective devices of the system 100 may support techniques for determining heart rate data of a user based on physiological data (e.g., athletic data) collected by the wearable device. The system may support techniques for determining heart rate data during periods of activity, exercise, and the like. In particular, the system 100 shown in fig. 1 may support techniques for determining heart rate data of the user 102 and causing a user device 106 corresponding to the user 102 to display an indication of the heart rate data. For example, as shown in FIG. 1, user 1 (user 102-a) may be associated with a wearable device 104-a (e.g., finger ring 104-a) and a user device 106-a. In this example, the ring 104-a may collect data associated with the user 102-a, including motion, temperature, heart rate, HRV, and the like. In some aspects, the ring 104-a may be used to collect physiological data of the user, which the ring 104-a may use to select actual heart rate data from motion artifacts. The finger ring 104-a may determine heart rate data based on PPG monitoring.

The physiological data collection, PPG monitoring, and heart rate data determination may be performed by any component of the system 100, including the ring 104-a, the user device 106-a associated with the user 1, the one or more servers 110, or any combination thereof. For example, in some implementations, the collected PPG data and motion data may be input into a machine learning model configured to determine a heart rate of the user. After determining the heart rate data, the system 100 may optionally cause the GUI of the user device 106-a to display all or a subset of the heart rate data.

Those skilled in the art will appreciate that one or more aspects of the present disclosure may be implemented in the system 100 to additionally or alternatively address other problems than those described above. Further, various aspects of the present disclosure may provide technical improvements to "conventional" systems or processes as described herein. However, the description and drawings include only example technical improvements resulting from implementing aspects of the present disclosure, and thus do not represent all technical improvements provided within the scope of the claims.

Fig. 2 illustrates an example of a system 200 supporting techniques for measuring heart rate in accordance with various aspects of the disclosure. System 200 may implement system 100 or be implemented by system 100. In particular, system 200 illustrates an example of a ring 104 (e.g., wearable device 104), user device 106, and server 110, as described with reference to fig. 1.

In some aspects, the ring 104 may be configured to be worn on a user's finger and one or more user physiological parameters may be determined when worn on the user's finger. Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveform, respiration rate, heart rate, HRV, blood oxygen level, and the like.

The system 200 further includes a user device 106 (e.g., a smart phone) in communication with the ring 104. For example, the finger ring 104 may be in wireless and/or wired communication with the user device 106. In some implementations, the finger ring 104 may send measured and processed data (e.g., temperature data, PPG data, motion/accelerometer data, finger ring input data, etc.) to the user device 106. User device 106 may also send data to ring 104, such as ring 104 firmware/configuration updates. The user device 106 may process the data. In some implementations, the user device 106 may transmit data to the server 110 for processing and/or storage.

The finger ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 may store or otherwise include various components of the ring including, but not limited to, device electronics, a power source (e.g., battery 210, and/or a capacitor), one or more substrates (e.g., a printable circuit board) interconnecting the device electronics and/or the power source, and the like. The device electronics may include device modules (e.g., hardware/software), such as: a processing module 230-a, a memory 215, a communication module 220-a, a power module 225, etc. The device electronics may also include one or more sensors. Example sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., PPG system 235), and one or more motion sensors 245.

The sensors may include association modules (not shown) configured to communicate with respective components/modules of the finger ring 104 and generate signals associated with the respective sensors. In some aspects, each of the components/modules of the finger ring 104 may be communicatively coupled to each other via a wired or wireless connection. Further, the finger ring 104 may include additional and/or alternative sensors or other components configured to collect physiological data from a user, including light sensors (e.g., LEDs), oximeter, etc.

The finger ring 104 shown and described with reference to fig. 2 is provided for illustrative purposes only. Thus, the finger ring 104 may include additional or alternative components such as those shown in FIG. 2. Other finger rings 104 may be fabricated that provide the functionality described herein. For example, a finger ring 104 with fewer components (e.g., sensors) may be manufactured. In a particular example, the finger ring 104 can be manufactured with a single temperature sensor 240 (or other sensor), a power source, and device electronics configured to read the single temperature sensor 240 (or other sensor). In another particular example, the temperature sensor 240 (or other sensor) may be attached to a user's finger (e.g., using a clamp, a spring-loaded clamp, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn computing device that reads the temperature sensor 240 (or other sensor). In other examples, the finger ring 104 may be manufactured to include additional sensors and processing functions.

The housing 205 may include one or more housing 205 assemblies. The housing 205 may include an outer housing 205-b assembly (e.g., an outer housing) and an inner housing 205-a assembly (e.g., a molded piece). The housing 205 may include additional components (e.g., additional layers) not explicitly shown in fig. 2. For example, in some implementations, the finger ring 104 may include one or more insulating layers that electrically insulate the device electronics and other conductive materials (e.g., electrical traces) from the outer housing 205-b (e.g., metal outer housing 205-b). The housing 205 may provide structural support for the device electronics, the battery 210, one or more substrates, and other components. For example, the housing 205 may protect the device electronics, the battery 210, and one or more substrates from mechanical forces, such as pressure and impact. The housing 205 may also protect the device electronics, the battery 210, and one or more substrates from water and/or other chemicals.

The outer housing 205-b may be made of one or more materials. In some implementations, the outer housing 205-b may include a metal, such as titanium, which may provide strength and wear resistance at a relatively light weight. The outer housing 205-b may also be made of other materials, such as polymers. In some implementations, the outer housing 205-b may be protective and decorative.

Inner housing 205-a may be configured to engage with a user's finger. The inner housing 205-a may be formed of a polymer (e.g., medical grade polymer) or other material. In some implementations, the inner housing 205-a may be transparent. For example, the inner housing 205-a may be transparent to light emitted by a PPG Light Emitting Diode (LED). In some implementations, the inner housing 205-a assembly may be molded onto the outer housing 205-b. For example, inner housing 205-a may include a polymer that is molded (e.g., injection molded) to fit into the metal housing of outer housing 205-b.

The finger ring 104 may include one or more substrates (not shown). The device electronics and battery 210 may be included on one or more substrates. For example, the device electronics and battery 210 may be mounted on one or more substrates. An example substrate may include one or more Printed Circuit Boards (PCBs), such as a flexible PCB (e.g., polyimide). In some implementations, the electronics/battery 210 may include a surface mounted device (e.g., a Surface Mount Technology (SMT) device) on a flexible PCB. In some implementations, one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between device electronics. The electrical traces may also connect the battery 210 to the device electronics.

The device electronics, battery 210, and substrate may be arranged in the finger ring 104 in various ways. In some implementations, one substrate including the device electronics may be mounted along the bottom (e.g., lower half) of the finger ring 104 such that the sensors (e.g., PPG system 235, temperature sensor 240, motion sensor 245, and other sensors) engage with the underside of the user's finger. In these implementations, a battery 210 may be included along a top portion of the finger ring 104 (e.g., on another substrate).

The various components/modules of the finger ring 104 represent functions (e.g., circuitry and other components) that may be included in the finger ring 104. A module may include any discrete and/or integrated electronic circuit component that implements analog and/or digital circuitry capable of producing the functionality attributed to the module herein. For example, the module may include analog circuitry (e.g., amplification circuitry, filtering circuitry, analog/digital conversion circuitry, and/or other signal conditioning circuitry). A module may also include digital circuitry (e.g., combinational or sequential logic circuitry, memory circuitry, etc.).

The memory 215 (memory module) of the ring 104 may include any volatile, non-volatile, magnetic, or dielectric medium, such as Random Access Memory (RAM), read Only Memory (ROM), non-volatile RAM (NVRAM), electrically Erasable Programmable ROM (EEPROM), flash memory, or any other memory device. Memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data (e.g., motion data, temperature data, PPG data) collected by the respective sensors and PPG system 235. Further, the memory 215 may include instructions that, when executed by one or more processing circuits, cause the module to perform the various functions attributed below to the module herein. The device electronics of the finger ring 104 described herein are merely example device electronics. Accordingly, the type of electronic components used to implement the device electronics may vary based on design considerations.

The functions attributed to the modules of finger ring 104 described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. Depiction of different features as modules is intended to highlight different functional aspects and does not necessarily imply that such modules must be realized by separate hardware/software components. Rather, functionality associated with one or more modules may be performed by separate hardware/software components or integrated within a common hardware/software component.

The processing module 230-a of the finger ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, system on a chip (SOC), and/or other processing devices. The processing module 230-a communicates with the modules contained in the finger ring 104. For example, the processing module 230-a may send/receive data to/from modules and other components of the ring 104 (such as sensors). As described herein, modules may be implemented by various circuit components. Thus, a module may also be referred to as a circuit (e.g., a communication circuit and a power circuit).

The processing module 230-a may be in communication with the memory 215. Memory 215 may include computer-readable instructions that, when executed by processing module 230-a, cause processing module 230-a to perform the various functions attributed to processing module 230-a herein. In some implementations, the processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication functions provided by the communication module 220-a (e.g., an integrated bluetooth low energy transceiver) and/or additional on-board memory 215.

The communication module 220-a may include circuitry that provides wireless and/or wired communication with the user device 106 (e.g., the communication module 220-b of the user device 106). In some implementations, the communication modules 220-a, 220-b may include wireless communication circuitry, such as Bluetooth circuitry and/or Wi-Fi circuitry. In some implementations, the communication modules 220-a, 220-b may include wired communication circuitry, such as Universal Serial Bus (USB) communication circuitry. Using the communication module 220-a, the ring 104 and the user device 106 may be configured to communicate with each other. The processing module 230-a of the finger ring may be configured to transmit/receive data to/from the user device 106 via the communication module 220-a. Example data may include, but is not limited to, athletic data, temperature data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates (e.g., state of charge, battery charge level, and/or finger ring 104 configuration settings). The processing module 230-a of the ring may also be configured to receive updates (e.g., software/firmware updates) and data from the user device 106.

The finger ring 104 may include a battery 210 (e.g., a rechargeable battery 210). Example batteries 210 may include lithium ion or lithium polymer batteries 210, but various battery 210 options are possible. The battery 210 may be charged wirelessly. In some implementations, the finger ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., battery 210 or capacitor) may have a curved geometry that matches the curve of the finger ring 104. In some aspects, the charger or other power source may include additional sensors that may be used to collect data in addition to or in addition to the data collected by the finger ring 104 itself. Further, the charger or other power source of the ring 104 may act as the user device 106, in which case the charger or other power source of the ring 104 may be configured to receive data from the ring 104, store and/or process the data received from the ring 104, and communicate the data between the ring 104 and the server 110.

In some aspects, the finger ring 104 includes a power module 225 that can control the charging of the battery 210. For example, the power module 225 may be engaged with an external wireless charger that charges the battery 210 when engaged with the finger ring 104. The charger may include a reference structure that mates with the reference structure of the ring 104 to create a designated orientation with the ring 104 during charging of the ring 104. The power module 225 may also regulate the voltage of the device electronics, regulate the power output to the device electronics, and monitor the state of charge of the battery 210. In some implementations, the battery 210 may include a Protection Circuit Module (PCM) that protects the battery 210 from high current discharge, over-voltage during charging of the finger ring 104, and under-voltage during discharging of the finger ring 104. The power module 225 may also include electrostatic discharge (ESD) protection.

One or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensor 240 may be configured to generate a temperature signal (e.g., temperature data) indicative of the temperature read or sensed by the temperature sensor 240. The processing module 230-a may determine the temperature of the user at the location of the temperature sensor 240. For example, in the finger ring 104, temperature data generated by the temperature sensor 240 may indicate a user temperature (e.g., skin temperature) at a user's finger. In some implementations, the temperature sensor 240 may contact the skin of the user. In other implementations, a portion of the housing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., a thin thermally conductive barrier) between the temperature sensor 240 and the skin of the user. In some implementations, the portion of the finger ring 104 configured to contact the user's finger may have a thermally conductive portion and a thermally insulating portion. The heat conducting portion may conduct heat from the user's finger to the temperature sensor 240. The thermally insulating portion may insulate portions of the finger ring 104 (e.g., the temperature sensor 240) from ambient temperature.

In some implementations, the temperature sensor 240 may generate a digital signal (e.g., temperature data) that the processing module 230-a may use to determine the temperature. As another example, where the temperature sensor 240 includes a passive sensor, the processing module 230-a (or the temperature sensor 240 module) may measure the current/voltage generated by the temperature sensor 240 and determine the temperature based on the measured current/voltage. Example temperature sensors 240 may include thermistors (such as Negative Temperature Coefficient (NTC) thermistors) or other types of sensors including resistors, transistors, diodes, and/or other electrical/electronic components.

The processing module 230-a may sample the temperature of the user over time. For example, the processing module 230-a may sample the temperature of the user according to a sampling rate. An example sampling rate may include one sample per second, but the processing module 230-a may be configured to sample the temperature signal at other sampling rates that are higher or lower than one sample per second. In some implementations, the processing module 230-a may continuously sample the user's temperature throughout the day and night. Sampling at a sufficient rate throughout the day (e.g., one sample per second) may provide sufficient temperature data to perform the analysis described herein.

The processing module 230-a may store the sampled temperature data in the memory 215. In some implementations, the processing module 230-a may process the sampled temperature data. For example, the processing module 230-a may determine an average temperature value over a period of time. In one example, the processing module 230-a may determine the average temperature value for each minute by summing all temperature values collected per minute and dividing by the number of samples in that minute. In the specific example where the temperature is sampled at one sample per second, the average temperature may be the sum of all sampled temperatures for one minute divided by sixty seconds. Memory 215 may store an average temperature value over time. In some implementations, the memory 215 may store an average temperature (e.g., one per minute) instead of the sampled temperature in order to conserve the memory 215.

The sampling rate that may be stored in the memory 215 may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other implementations, the sampling rate may vary throughout the day/night. In some implementations, the finger ring 104 may filter/reject temperature readings, such as large spikes in temperature that are not indicative of physiological changes (e.g., temperature spikes from a thermal shower). In some implementations, the finger ring 104 may filter/reject temperature readings that may be unreliable due to other factors, such as excessive motion during 104 exercise (e.g., as indicated by the motion sensor 245).

The finger ring 104 (e.g., a communication module) may transmit the sampled temperature data and/or average temperature data to the user device 106 for storage and/or further processing. The user device 106 may transmit the sampled temperature data and/or average temperature data to the server 110 for storage and/or further processing.

Although the finger ring 104 is shown as including a single temperature sensor 240, the finger ring 104 may include multiple temperature sensors 240 in one or more locations, such as disposed near a user's finger along the inner housing 205-a. In some implementations, the temperature sensor 240 may be a stand-alone temperature sensor 240. Additionally or alternatively, one or more temperature sensors 240 may be included with (e.g., packaged with) other components, such as with an accelerometer and/or a processor.

The processing module 230-a may acquire and process data from multiple temperature sensors 240 in a similar manner as described with respect to a single temperature sensor 240. For example, the processing module 230 may sample, average, and store temperature data from each of the plurality of temperature sensors 240 separately. In other examples, the processing module 230-a may sample the sensors at different rates and average/store different values for different sensors. In some implementations, the processing module 230-a may be configured to determine a single temperature based on an average of two or more temperatures determined by two or more temperature sensors 240 in different locations on the finger.

The temperature sensor 240 on the ring 104 may acquire the distal temperature at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 may acquire the temperature of the user from the underside of the finger or at different locations on the finger. In some implementations, the finger ring 104 may continuously acquire the distal temperature (e.g., at a sampling rate). Although the distal temperature measured by the finger ring 104 at the finger is described herein, other devices may measure temperatures at the same/different locations. In some cases, the temperature of the distal end measured at the user's finger may be different from the temperature measured at the user's wrist or other external body location. Further, the distal temperature (e.g., the "skin" temperature) measured at the user's finger may be different from the core temperature of the user. As such, the finger ring 104 may provide a useful temperature signal that may not be acquired at other internal/external locations of the body. In some cases, continuous temperature measurements at the finger may capture temperature fluctuations (e.g., small fluctuations or large fluctuations) that may not be apparent in the core temperature. For example, continuous temperature measurements at the finger may capture one minute-minute or one hour-hour temperature fluctuations that provide additional insight that other temperature measurements elsewhere in the body may not provide.

The finger ring 104 may include a PPG system 235.PPG system 235 may include one or more light emitters that emit light. The PPG system 235 may also include one or more light receivers that receive light emitted by the one or more light emitters. The light receiver may generate a signal (hereinafter referred to as a "PPG" signal) indicative of the amount of light received by the light receiver. The light emitters may illuminate an area of the user's finger. The PPG signal generated by PPG system 235 may be indicative of the perfusion of blood in the illuminated region. For example, the PPG signal may be indicative of a change in blood volume in the illuminated region caused by the user's pulse pressure. The processing module 230-a may sample the PPG signal and determine a pulse waveform of the user based on the PPG signal. The processing module 230-a may determine various physiological parameters, such as the user's respiratory rate, heart rate, HRV, oxygen saturation, and other cycle parameters, based on the user's pulse waveform.

In some implementations, the PPG system 235 may be configured to reflect the PPG system 235, wherein the one or more light receivers receive transmitted light reflected by an area of the user's finger. In some implementations, PPG system 235 may be configured to transmit PPG system 235, with one or more light emitters and one or more light receivers arranged opposite each other such that light is transmitted directly through a portion of a user's finger to one or more light receivers.

The number and ratio of transmitters and receivers included in PPG system 235 may vary. An example light emitter may include a Light Emitting Diode (LED). The light emitters may emit light in the infrared spectrum and/or other spectrums. Example optical receivers may include, but are not limited to, photosensors, phototransistors, and photodiodes. The optical receiver may be configured to generate the PPG signal in response to a wavelength received from the optical transmitter. The locations of the transmitter and receiver may vary. Furthermore, a single device may include a reflective and/or transmissive PPG system 235.

In some implementations, the PPG system 235 shown in fig. 2 may include a reflective PPG system 235. In these implementations, PPG system 235 may include a centrally located light receiver (e.g., at the bottom of finger ring 104) and two light emitters located on each side of the light receiver. In implementations herein, PPG system 235 (e.g., an optical receiver) may generate a PPG signal based on light received from one or both of the optical emitters. In other implementations, other placements, combinations, and/or configurations of one or more optical transmitters and/or optical receivers are contemplated.

The processing module 230-a may control one or both of the optical transmitters to emit light while sampling the PPG signal generated by the optical receiver. In some implementations, the processing module 230-a may cause the optical transmitter with the stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, when the PPG signal is sampled at a sampling rate (e.g., 250 Hz), the selected light emitters may continuously emit light.

Sampling the PPG signal generated by PPG system 235 may produce a pulse waveform, which may be referred to as "PPG. The pulse waveform may indicate the blood pressure versus (vs) time for a plurality of cardiac cycles. The pulse waveform may include peaks indicative of cardiac cycles. Further, the pulse waveform may include a breath-induced variation that may be used to determine the respiration rate. In some implementations, the processing module 230-a can store the pulse waveform in the memory 215. The processing module 230-a may process the pulse waveform as it is generated and/or from the memory 215 to determine the user physiological parameters described herein.

The processing module 230-a may determine the heart rate of the user based on the pulse waveform. For example, the processing module 230-a may determine a heart rate (e.g., in beats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as the heart beat interval (IBI). The processing module 230-a may store the determined heart rate value and IBI value in the memory 215.

The processing module 230-a may determine the HRV over time. For example, the processing module 230-a may determine the HRV based on the change in IBI. The processing module 230-a may store HRV values over time in the memory 215. Further, the processing module 230-a may determine a respiration rate of the user over time. For example, the processing module 230-a may determine the respiration rate based on a frequency modulation, an amplitude modulation, or a baseline modulation of the user's IBI value over a period of time. The respiration rate may be calculated as a breath per minute or as another respiration rate (e.g., every 30 seconds). The processing module 230-a may store the user respiratory rate values over time in the memory 215.

The finger ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and/or one or more gyroscopes (gyros). The motion sensor 245 may generate a motion signal indicative of the motion of the sensor. For example, the finger ring 104 may include one or more accelerometers that generate acceleration signals indicative of acceleration of the accelerometers. As another example, the finger ring 104 may include one or more gyroscopic sensors that generate a gyroscope signal indicative of angular motion (e.g., angular velocity) and/or orientation changes. The motion sensor 245 may be included in one or more sensor packages. An example accelerometer/gyroscope sensor is a Bosch BM1160 inertial microelectromechanical system (MEMS) sensor that can measure angular rate and acceleration in three perpendicular axes.

The processing module 230-a may sample the motion signal at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signal. For example, the processing module 230-a may sample the acceleration signal to determine the acceleration of the ring 104. As another example, the processing module 230-a may sample the gyroscope signal to determine angular motion. In some implementations, the processing module 230-a may store the motion data in the memory 215. The motion data may include sampled motion data and motion data calculated based on the sampled motion signals (e.g., acceleration and angle values).

The finger ring 104 may store various data described herein. For example, the finger ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data (e.g., average temperature). As another example, the finger ring 104 may store PPG signal data, such as pulse waveforms and data calculated based on pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory rate values). The finger ring 104 may also store motion data, such as sampled motion data indicative of line and angular motion.

The finger ring 104 or other computing device may calculate and store additional values based on the sampled/calculated physiological data. For example, the processing module 230 may calculate and store various metrics, such as sleep metrics (e.g., sleep scores), activity metrics, and readiness metrics. In some implementations, the additional value/metric may be referred to as a "derived value". The finger ring 104 or other computing/wearable device may calculate various values/metrics regarding motion. Example derived values of motion data may include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalents of task values (MET), and orientation values. The motion count, regularity value, intensity value, and MET may indicate the amount of user motion (e.g., speed/acceleration) over time. The orientation value may indicate how the ring 104 is oriented on the user's finger and whether the ring 104 is worn on the left hand or the right hand.

In some implementations, the motion count and regularity value may be determined by counting the number of acceleration peaks over one or more time periods (e.g., one or more time periods of 30 seconds to 1 minute). The intensity value may indicate the number of movements and the associated intensity of the movements (e.g., acceleration value). Depending on the associated threshold acceleration value, the intensity values may be classified as low, medium, and high. MET may be determined based on the intensity of movement during a period of time (e.g., 30 seconds), the regularity/irregularity of movement, and the number of movements associated with different intensities.

In some implementations, the processing module 230-a may compress the data stored in the memory 215. For example, the processing module 230-a may delete sampled data after computation based on the sampled data. As another example, the processing module 230-a may average the data over a longer period of time in order to reduce the number of stored values. In a particular example, if the average temperature of the user over a minute is stored in the memory 215, the processing module 230-a may calculate the average temperature over a five minute period for storage and then erase the one minute average temperature data. The processing module 230-a may compress the data based on various factors, such as the total amount of memory 215 used/available and/or the time elapsed since the ring 104 last transferred the data to the user device 106.

While the physiological parameters of the user may be measured by sensors included on the finger ring 104, other devices may also measure the physiological parameters of the user. For example, while the temperature of the user may be measured by the temperature sensor 240 included in the finger ring 104, other devices may also measure the temperature of the user. In some examples, other wearable devices (e.g., wrist devices) may include sensors that measure physiological parameters of the user. Furthermore, medical devices such as external medical devices (e.g., wearable medical devices) and/or implantable medical devices may measure physiological parameters of a user. The techniques described herein may be implemented using one or more sensors on any type of computing device.

The physiological measurements may be continuously taken throughout the day and/or night. In some implementations, physiological measurements may be taken during various portions of the day and/or 104 portions of the night. In some implementations, physiological measurements may be obtained in response to determining that the user is in a particular state (e.g., an active state, a resting state, and/or a sleep state). For example, the finger ring 104 may take physiological measurements in a resting/sleep state to obtain a cleaner physiological signal. In one example, the finger ring 104 or other device/system may detect when the user is resting and/or sleeping and acquire a physiological parameter (e.g., temperature) of the detected state. The device/system may use rest/sleep physiological data and/or other data when the user is in other states in order to implement the techniques of this disclosure.

In some implementations, the finger ring 104 may be configured to collect, store, and/or process data, as previously described herein, and may transmit any of the data described herein to the user device 106 for storage and/or processing. In some aspects, user device 106 includes a wearable application 250, an Operating System (OS), a web browser application (e.g., web browser 280), one or more additional applications, and a GUI 275. The user device 106 may further include other modules and components including sensors, audio devices, haptic feedback devices, and the like. Wearable application 250 may include an example of an application program (e.g., an "app") that may be installed on user device 106. The wearable application 250 may be configured to obtain data from the ring 104, store the obtained data, and process the obtained data, as described herein. For example, wearable application 250 may include User Interface (UI) module 255, acquisition module 260, processing module 230-b, communication module 220-b, and a storage module (e.g., database 265) configured to store application data.

The various data processing operations described herein may be performed by the ring 104, the user device 106, the server 110, or any combination thereof. For example, in some cases, data collected by the ring 104 may be preprocessed and transmitted to the user device 106. In the examples herein, the user device 106 may perform some data processing operations on the received data, may transmit the data to the server 110 for data processing, or both. For example, in some cases, the user device 106 may perform processing operations requiring relatively low processing power and/or operations requiring relatively low latency (latency), while the user device 106 may transmit data to the server 110 for processing operations requiring relatively high processing power and/or operations that may allow relatively high latency.

In some aspects, the finger ring 104, user device 106, and server 110 of the system 200 may be configured to evaluate the sleep mode of the user. In particular, respective components of system 200 may be used to collect data from a user via finger ring 104 and generate one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. For example, as previously noted herein, the finger ring 104 of the system 200 may be worn by a user to collect data from the user, including temperature, heart rate, HRV, and the like. The data collected by the finger ring 104 may be used to determine when the user is asleep to assess the user's sleep for a given "sleep day". In some aspects, a score for each respective sleep day may be calculated for the user such that a first sleep day is associated with a first set of scores and a second sleep day is associated with a second set of scores. The score for each respective sleep day may be calculated based on data collected by finger ring 104 during the respective sleep day. The score may include, but is not limited to, a sleep score, a readiness score, and the like.

In some cases, a "sleep day" may be aligned with a traditional calendar day such that a given sleep day extends from midnight to midnight of the respective calendar day. In other cases, the sleep day may be offset relative to the calendar day. For example, the sleep day may be from 6 pm on calendar days: 00 (18:00) to 6 pm for the subsequent calendar day: 00 (18:00). In this example, 6 pm: 00 may act as a "cutoff time", where at 6 pm: data collected from the user before 00 is counted for the current sleep day and at 6 pm: data collected from the user after 00 is counted for the subsequent sleep day. Due to the fact that most individuals sleep most at night, shifting the sleep day relative to the calendar day may enable system 200 to evaluate the user's sleep pattern in a manner consistent with their sleep schedule. In some cases, a user may be able to selectively adjust (e.g., via a GUI) the timing of the sleep day relative to the calendar day such that the sleep day is aligned with the duration of normal sleep of the respective user.

In some implementations, each total score (e.g., sleep score, readiness score) of a user on each respective day may be determined/calculated based on one or more "contributors," factors, "or" contributors. For example, a total sleep score for a user may be calculated based on a set of contributors, including: total sleep, efficiency, calm, REM sleep, deep sleep, latency, timing, or any combination thereof. Sleep scores may include any number of contributors. A "total sleep" contributor may refer to the sum of all sleep periods of a sleep day. The "efficiency" contributor may reflect the percentage of time spent sleeping compared to the time spent waking up while sleeping, and may be calculated using an average of the efficiency of long sleep periods (e.g., the main sleep period) of the sleep day weighted by the duration of each sleep period. The "calm (restfulness)" contributor may indicate how calm the user's sleep is, and may be calculated using an average of all sleep periods of the sleep day weighted by the duration of each period. The calm contributor may be based on a "wake count" (e.g., the sum of all wakefulness detected during different sleep periods (when the user wakes up)), excessive movement, and a "wake count" (e.g., the sum of all wake detected during different sleep periods (when the user gets out of bed)).

A "REM sleep" contributor may refer to the sum of REM sleep duration over all sleep periods including the sleep day of REM sleep. Similarly, a "deep sleep" contributor may refer to the sum of the duration of deep sleep over all sleep periods including the sleep day of deep sleep. The "waiting time" contributors may represent how long a user spends (e.g., average, median, longest) in going to sleep, and may be calculated using an average of long sleep periods during the sleep day, weighted by the duration of each period and the number of such periods (e.g., merging a given one or more sleep stages may be its own contributor or may weight other contributors). Finally, a "timing" contributor may refer to the relative timing of sleep periods within a sleep day and/or calendar day, and may be calculated using an average of all sleep periods of the sleep day weighted by the duration of each period.

As another example, an overall readiness score for a user may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, restitution index, temperature, activity balance, or any combination thereof. The readiness score may include any number of contributors. A "sleep" contributor may refer to a combined sleep score for all sleep periods within a sleep day. A "sleep balance" contributor may refer to the cumulative duration of all sleep periods within a sleep day. In particular, sleep balance may indicate to a user whether sleep that user has performed within a certain duration (e.g., the last two weeks) is balanced with the user's needs. Typically, adults need 7-9 hours of sleep every night to remain healthy, alert, and perform best both mentally and physically. However, an occasional night with poor sleep is normal, so sleep balance contributors consider long-term sleep patterns to determine whether the sleep needs of each user are met. The "resting heart rate" contributor may indicate a lowest heart rate from a longest sleep period of a sleep day (e.g., a primary sleep period) and/or a lowest heart rate from a nap that occurs after the primary sleep period.

With continued reference to the "contributors" (e.g., factors, contributors) to the readiness score, the "HRV balance" contributors may indicate the highest HRV average from the primary sleep period and the naps that occur after the primary sleep period. HRV balance contributors may help users track their recovery status by comparing their HRV trend over a first period of time (e.g., two weeks) to the average HRV over a second, some longer period of time (e.g., three months). The "recovery index" contributors may be calculated based on the longest sleep period. The recovery index measures how long it takes for the user's resting heart rate to settle at night. A very good recovery is marked by the user's resting heart rate stabilizing during the first half of the night (at least six hours before the user wakes up), leaving the body to recover for the next day. If the maximum temperature of the user during the nap is at least 0.5 ℃ higher than the maximum temperature during the longest period, the "body temperature" contributor may be calculated based on the longest sleep period (e.g., the main sleep period) or based on the nap occurring after the longest sleep period. In some aspects, the finger ring may measure the body temperature of the user while the user is asleep, and the system 200 may display the average temperature of the user relative to the user's baseline temperature. If the body temperature of the user is outside of their normal range (e.g., clearly above or below 0.0), the body temperature contributor may be highlighted (e.g., enter an "attention" state) or otherwise generate an alert for the user.

In some aspects, the system 200 may support techniques for determining heart rate data of a user based on physiological data (e.g., athletic data) collected by a wearable device. In some aspects, the ring 104, user device 106, and server 110 of the system 200 may be configured to determine heart rate data of the user during a period of motion, activity, exercise, or the like. In particular, the respective components of the system 200 may be used to determine heart rate data (e.g., exercise heart rate data) of a user based on physiological data (e.g., exercise) of the user. For example, the respective components of the system 200 may collect physiological data of the user, which the finger ring 104-a (or other components of the system 200) may use to select actual heart rate data and remove or ignore motion artifacts that may lead to false heart rate measurements. As such, physiological data may be obtained by utilizing sensors on the finger ring 104 of the system 200.

For example, as previously noted herein, the finger ring 104 of the system 200 may be worn by a user to collect data from the user, including temperature, heart rate, movement, and the like. The finger ring 104 of the system 200 may collect physiological data from a user based on arterial blood flow. The physiological data collected by the ring 104 may be used to determine false or erroneous heart rate data due to motion (e.g., motion artifacts) rather than actual blood flow, and thus may be used to determine actual heart rate data.

For example, in some implementations, the acquired PPG data and motion data may be input into a machine learning model (e.g., heuristic-based model, deep learning model, regression-based model) configured to determine a heart rate of the user. In such cases, the machine learning model may be configured to distinguish between candidate heart rate measurements attributable to motion artifacts and candidate heart rate measurements indicative of the actual heart rate of the user. Additionally or alternatively, the machine learning model may be configured to identify time and/or frequency domain features within the received PPG data and motion data (which may be used to identify candidate heart rate measurements), and may be configured to determine or estimate a heart rate of the user based on the identified features.

The process for determining heart rate data (e.g., athletic heart rate data) may be further illustrated and described with reference to fig. 3 and 4.

Fig. 3 illustrates an example of a heart rate determination process 300 supporting techniques for measuring heart rate in accordance with aspects of the present disclosure. The heart rate determination process 300 may implement aspects of the system 100, the system 200, or a combination thereof, or by aspects of the system 100, the system 200, or a combination thereof. For example, in some implementations, the heart rate determination process 300 may generate heart rate data (e.g., activity heart rate data, exercise heart rate data) that may be displayed to the user via the GUI 275 of the user device 106, as shown in fig. 2.

As described herein, a system (e.g., system 200) or a portion of system 200 (e.g., wearable device (e.g., ring 104)) may identify heart rate data of a user from a set of heart rate data, where the heart rate data may include motion artifacts. In some cases, the wearable device may detect heart rate data of the user during periods of activity, motion, exercise, etc., according to techniques described herein. In some cases, the techniques described herein for determining heart rate data may be used for heart rate detection other than activity heart rate detection, or for all heart rate detection (e.g., whether a user is active or moving). Accordingly, heart rate determination process 300 may not be limited to determining a user's heart rate during exercise, sports, activities, and the like. In some cases, the wearable device may detect that the user is performing an activity (e.g., an activity that satisfies a movement threshold, an activity that results in the heart rate meeting a heart rate threshold), exercise, etc., and may perform heart rate determination process 300 described herein based on detecting the activity. In some cases, the user may notify the wearable device that the user is performing such activity or otherwise prompt the wearable device to perform heart rate determination process 300 described herein.

At 305, the wearable device may measure motion data (e.g., acceleration data) associated with the user. In some cases, the wearable device may continuously, periodically (e.g., according to periodicity) measure motion data based on activity, time of day, etc. For example, the wearable device may be configured to measure motion data (e.g., motion data points) every second. In some cases, the wearable device may be configured to obtain motion data and perform the processes described herein based on a user of the wearable device performing an activity (e.g., an activity such as meeting a movement threshold), an exercise, and so on. During such activities, the wearable device may be configured to measure motion data according to periodicity (such as for each second of duration of the activity).

In some cases, the wearable device may measure motion for a configured duration, wherein the wearable device may be configured with or receive an indication of the configured duration, or may determine the configured duration. In some cases, the duration may be based on the duration of an activity, exercise, etc., performed by the user. To measure motion data, the wearable device may utilize one or more motion sensors (e.g., motion sensor 245) on the wearable device. In some cases, the motion data may refer to acceleration data. In such a case, the motion sensor on the wearable device may refer to an accelerometer (e.g., a 3D accelerometer) capable of detecting an acceleration of the user (such as a 3D acceleration). The wearable device may obtain motion data in the x-axis, y-axis, and z-axis, where each axis may be referred to as a different motion channel. Thus, the wearable device may obtain motion data through three motion channels. In some cases, the accelerometer may measure acceleration of the user at 50Hz or some other frequency.

At 310, the wearable device, the user device, the server, or any combination thereof may pre-process the motion data. For example, the wearable device may pre-process the 3D acceleration data (e.g., raw data) to obtain processed acceleration data. The wearable device may preprocess each motion channel separately. In some cases, preprocessing may include removing outliers, erroneous data, noise, or a combination thereof from the motion data.

At 315, the wearable device, the user device, the server, or any combination thereof may perform a multipath combining process. As described, the wearable device may obtain motion data via three channels, x, y, and z. Thus, the wearable device may input data associated with three separate channels into the multipath combiner module to obtain a single channel (e.g., a single set of motion data). In some cases, the wearable device may be configured to select one motion channel from the set of motion channels (e.g., select an x-axis motion channel, a y-axis motion channel, or a z-axis motion channel). The selection may be based on quality such that the wearable device may select a channel associated with a highest quality, a least number of outliers, a least amount of noise, or some combination thereof.

In some cases, the wearable device, user device, server, or any combination thereof may combine two or more motion channels using any mathematical operation (including averaging operations, weighted averaging operations, etc.) to generate aggregated or composite motion data. For example, the wearable device may average at least two of the channels. In some cases, the wearable device may average the two channels associated with the highest quality (e.g., associated with the least amount of outliers, noise, etc.). In some cases, the wearable device may be configured to average any of the channels if each of the channels meets a quality threshold. In some cases, the wearable device may be configured to average a particular number of channels. For example, the wearable device may be configured to average all three motion channels to obtain an average channel. Thus, the output of the multipath combiner module is a single motion channel. At 320, the wearable device, user device, server, or any combination thereof may input motion data (e.g., preprocessed and combined/synthesized motion data) into a temporal processor module. The temporal processor module may calculate an intensity associated with the motion data (e.g., a motion intensity), a change in the motion intensity (e.g., a rate of change in intensity), or both. The temporal processor module may output motion intensity data over time, or output motion intensity variations over time. In some cases, calculating the motion intensity and/or the rate of change of intensity may include calculating an absolute value of the motion data and calculating a mean of the absolute values. In some cases, the wearable device may input temporal motion data into a temporal processor module, and the module may output frequency domain motion data, and vice versa. In other words, components of system 200 may convert motion data into a time/frequency domain representation of the motion data.

At 325, the wearable device may obtain PPG data. The wearable device may sample the PPG under a set of conditions, such as frequency (e.g., 50 Hz). In some cases, the wearable device may continuously, periodically (e.g., according to periodicity) measure PPG data based on activity, time of day, etc. For example, the wearable device may be configured to measure PPG data (e.g., collect PPG data points) every second. In some cases, the wearable device may be configured to obtain PPG data and perform the processes described herein based on a user of the wearable device performing an activity (e.g., during a time when the activity meets a movement threshold), exercising, and so on. During such activity, the wearable device may be configured to measure PPG data according to periodicity (e.g., every second for the duration of the activity).

In some cases, the wearable device may measure motion over a configured duration, wherein the wearable device may be configured with or receive an indication of the configured duration or may determine the configured duration. In some cases, the duration may be based on the duration of an activity, exercise, etc., performed by the user. To measure PPG data, the wearable device may sample PPG data of the user using one or more sets of PPG sensors, where each set of PPG sensors includes at least one light source (e.g., LED) and at least one photodetector. For example, the wearable device may include a first pair of PPG sensors including a first LED and a first photodetector and a second pair of PPG sensors including a second LED and a second photodetector. In other words, the wearable device may include two separate "channels" for acquiring PPG data (e.g., two separate PPG signals from two respective sensor pairs). In some cases, the wearable device may sample PPG data using the first pair of PPG sensors and the second pair of PPG sensors simultaneously. Additionally or alternatively, the wearable device may sample PPG data sequentially using the first set of PPG sensors and the second set of PPG sensors. For example, the wearable device may sequentially control the activation states of the first pair of PPG sensors and the second pair of PPG sensors (e.g., when the second pair is in an inactive state, the first pair is in an active state, and vice versa).

In some implementations, the wearable device may sample the PPG using components across multiple sets of sensors. For example, the wearable device may obtain PPG data using the first LED and the second photodetector, or using the second LED and the first photodetector, or both. In some cases, the first LED and the second photodetector may be opposite each other on the wearable device. Thus, in a wearable device comprising two pairs of PPG sensors, the wearable device may obtain four PPG signals (e.g., four PPG channels). Sequentially activating separate PPG sensor groups/PPG sensor pairs may improve the quality and accuracy of each respective PPG signal, reduce interference, and may result in more accurate heart rate measurements.

At 330, the wearable device, user device, server, or any combination thereof may pre-process the PPG data. For example, the wearable device may filter the PPG data, such as to remove erroneous samples, outliers, noise, and the like. The wearable device may pre-process each PPG channel individually.

At 335, the wearable device, the user device, the server, or any combination thereof may perform a multipath combining process. As described, the wearable device may monitor PPG signals from multiple sensor groups. For example, the wearable device may obtain four PPG signals (e.g., PPG signals collected via four separate channels). Thus, the wearable device may input data associated with the four separate channels into the multipath combiner module to obtain a single channel (e.g., a single set of PPG data).

In some cases, the wearable device may be configured to select one PPG channel from a set of PPG channels. The selection may be based on quality such that the wearable device may select the channel associated with the highest quality, the least number of outliers, noise, etc. For example, in the context of a ring wearable device, each of the respective sensors (e.g., LEDs, photodetectors) for PPG sampling may be positioned at different radial positions along the inner circumference of the ring. Thus, one of the PPG signals from one or more of the sets of PPG sensors may be more reliable than PPG signals obtained from other ones of the plurality of sensors, such as due to the relative quality of the sensors' locations around and relative to the user, skin contact with each respective sensor or LED, and so on. For example, a sensor located on the underside of a user's finger (e.g., palm side) may produce more accurate PPG data than a sensor located on the bones of the finger (e.g., back side of the back of the hand). As another example, the relative positioning of the wearable device may result in less skin contact at one first sensor than the second sensor, and thus may result in PPG data of lower quality than the second sensor. As such, the output of the multipath combiner module may be based on the quality of the signals generated by each pair of sensors.

In some cases, the wearable device may combine two or more PPG channels/signals using any mathematical operation (including averaging operations, weighted averaging operations, etc.) to generate aggregated or composite PPG data. For example, the wearable device may average at least two of the PPG channels. In some cases, the wearable device may average the first two or three PPG channels associated with the highest quality (e.g., associated with the least amount of outliers, noise, etc.). In some cases, the wearable device may be configured to average any number of channels if each of the channels meets a quality threshold. In some cases, the wearable device may be configured to average a particular number of channels. For example, the wearable device may be configured to average all four PPG channels to obtain an average PPG channel. Thus, the output of the multipath combiner module is a single PPG signal/channel (e.g., a composite PPG signal). In some aspects, components of system 200 may convert PPG data (e.g., a synthesized PPG signal) into a time/frequency domain representation of the PPG data.

At 340, the wearable device, the user device, the server, or any combination thereof may perform heart rate detection. The wearable device may estimate a heart rate of the user based on the obtained PPG signal. Each time the user's heart beats, blood is pumped out to the arteries in the hand and fingers. A PPG sensor in a wearable device (e.g., the PPG sensor of PPG system 235 described with reference to fig. 2) can detect these changes in blood flow and blood volume using light reflection and absorption. Each pulse causes the arteries in the finger to alternate between distending and constricting (e.g., expanding and contracting). The change in light reflected back from the wave volume of red blood cells in the artery is measured by shining light onto the skin, such as via an LED. From here, the PPG may represent these blood flow changes by visual waveforms representing the activity of the user's heart and thus representing the heart rate. In some cases, the wearable device may determine the heart rate of the user based on the PPG signal meeting a quality threshold or based on a portion of the PPG signal meeting the quality threshold. For example, the wearable device may determine one or more portions of the PPG signal that are actively representative of the heart rate of the user.

As described herein, some activities may generate a PPG signal, which may be misinterpreted as a heart rate signal. For example, exercises such as running, jumping, etc. can cause motion artifacts. The optical properties of the PPG sensor may make PPG measurements susceptible to motion artifacts from variable and discontinuous contact between the device and the skin. Motion artifacts are typically caused by blood flow velocity changes caused by exercise motion or relative motion between the PPG sensor and the user's skin. For example, some movements may result in periodic pressure between the wearable device (e.g., a sensor of the wearable device) and the user site where the wearable device is located, and thus may apply periodic pressure to the blood vessel. The cyclic pressure may cause the blood vessel to periodically deflate and expand. Thus, the wearable device may detect such constriction and dilation of the blood vessel as an artificial PPG signal (e.g., motion artifact) because the constriction and dilation are due to the user's motion and not the user's heart rate.

Accordingly, to perform heart rate detection, the wearable device may compare PPG data with motion data. For example, the wearable device may detect multiple strong PPG signals in a given time interval. The plurality of PPG signals may represent a plurality of "candidate" heart rates of the user over a given time interval, where one of the candidate heart rates represents the actual heart rate of the user and the other candidate heart rates may be due to motion (e.g., an artificial heart rate or motion artifact). Thus, the system 200 may be configured to identify which candidate PPG signal represents the actual heart rate of the user, and may be configured to cancel or otherwise ignore PPG signals attributable to motion.

In some aspects, the system 200 may use motion data (such as combined motion data, motion intensity data, intensity rate of change data, or a combination thereof) to determine which of the plurality of strong PPG signals (e.g., candidate PPG signals) are due to motion and which PPG signal represents the actual heart rate of the user. For example, the system 200 may determine that the motion data collected during a given time interval exhibits a relative periodicity (e.g., frequency pattern) that is similar to or the same as the periodicity/frequency pattern exhibited by one of the candidate heart rates. In this example, the system 200 may identify candidate heart rates exhibiting a periodicity/frequency pattern similar to the motion data as motion artifacts, and thus may ignore the identified candidate heart rates/motion artifacts for the purpose of identifying the actual heart rate of the user. In some cases, the wearable device may analyze the motion data and PPG data by unit so that the wearable device may determine at which data points the other PPG signals overlap with the motion signal. The wearable device may remove PPG signals that overlap with or are otherwise attributable to motion (e.g., remove motion artifacts).

However, the blood vessels of healthy humans are soft and elastic, and they have harmonic frequencies when waves pass through them. The harmonic frequencies are integer multiples of the fundamental frequency. In some cases, some users (e.g., athletes) may exercise based on the pulse harmonic frequency so that the user may match their cadence to the harmonic frequency of their pulse. Thus, in some cases, the wearable device may not be able to remove all PPG signals that overlap with the motion signal, as the overlapping PPG signals may be the actual heart rate signal that the user exactly matches his cadence. Thus, after determining the overlapping PPG and motion signals, the wearable device may additionally analyze the data to determine whether to remove the overlapping PPG signals.

In some aspects, the system 200 may identify one or more candidate heart rates (e.g., candidate heart rate measurements) as motion artifacts based on a comparison of data trends between the candidate heart rate measurements and the motion data. For example, if the motion data increases periodically, the system 200 may identify candidate heart rate measurements that show an increased heart rate as motion artifacts due to trend comparisons between the motion data and the candidate heart rate data. In some cases, the wearable device may determine whether the motion data is stable, increasing, decreasing, etc. (e.g., trends in heart rate data) and may use such information to determine dynamic heart rate limits (e.g., ranges). The wearable device may use a heart rate limit (e.g., an expected range of human heart rates) to further determine which of the plurality of PPG signals are removed and determine the true heart rate signal. In other words, the wearable device may compare the PPG signal to a heart rate limit or range and may eliminate PPG signals that do not fall within the expected heart rate limit or range (e.g., candidate heart rates having periodicity/frequency patterns that fall outside of the expected range of human heart rates may be marked or identified as motion artifacts).

Upon determining the true heart rate signal (e.g., identifying candidate PPG signals representing the actual heart rate of the user), the wearable device may detect gaps in the heart rate data. For example, with respect to a ring device, PPG sensors located around the ring may also rotate as the ring rotates, and in some cases may not detect a signal (e.g., due to the sensors being located above the bone). Thus, in some cases, the wearable device may identify heart rate data on either side of the gap to interpolate the heart rate data into the gap. For example, the wearable device may analyze heart rate data during a first time (e.g., just before the gap) and analyze heart rate data during a second time (e.g., just after the gap). Based on the analysis, such as trends, intensities, or both, of heart rate data and/or motion data during the first time and the second time, the wearable device may interpolate heart rate data into the gap. For example, if the intensity of the motion data increases during the gap, it is expected that the heart rate of the user will also increase due to the increased intensity of the motion. As such, the system 200 may perform interpolation across the gaps to represent the heart rate that increased due to the corresponding increase in motion intensity.

In additional or alternative implementations, the system 200 may utilize an "estimator" or machine learning model/algorithm (e.g., heuristic-based model, deep learning model, regression-based model) to perform heart rate detection at 340. In particular, the motion data collected/processed at 305-320 and the PPG data collected and processed at 325-335 may be input into a machine learning model, wherein the machine learning model is configured to determine a heart rate of the user (e.g., select or estimate candidate heart rates from the set of candidate heart rates).

In some cases, to train a machine learning model for heart rate detection, the wearable device may acquire physiological data (e.g., PPG data, motion data) from one or more users (e.g., while the user is exercising), and the ECG-based device may be used to acquire heart rate data of the respective users (e.g., during the same time interval). In such cases, the ECG-based heart rate data may be used as a "true value (ground truth)" heart rate measurement for training a machine learning model.

In this example, physiological data (e.g., PPG data, heart rate data) may be input into a machine learning model, where the machine learning model is trained to generate or output ECG-based heart rate data based on received physiological data acquired from one or more wearable devices. In other words, the ECG-based data may be considered as the expected output of the model, where the machine learning model is trained to match (or closely estimate) the ECG-based heart rate data based on the received physiological data. As such, the machine learning model may be trained to receive PPG data and motion data as inputs and generate heart rate measurements/estimates as outputs.

In some cases, the system 200 may train multiple versions of the machine learning model, such as for different demographics of the user (e.g., different age groups, different activity/performance levels, different skin tones, etc.), for users with different medical conditions, and so on. In this regard, different models tailored to different demographics of the user may be used to further fine tune the ability of the respective model to perform heart rate detection. For example, the system 200 may obtain physiological data from a user that is a fanciful runner, and may perform heart rate detection for the user using a machine learning model that trains data from other runners.

After training the machine learning model, the machine learning model may be used to perform "on-site" heart rate measurements of the user based on physiological data (e.g., PPG data, motion data) acquired from the wearable device. For example, the time/frequency domain motion data and the time/frequency PPG data shown in fig. 3 may be input into a machine learning model at 340, wherein the machine learning model is configured to output heart rate measurements/estimates based on the received data.

For example, the machine learning model may be configured to distinguish between candidate heart rate measurements due to motion artifacts and candidate heart rate measurements indicative of an actual heart rate of the user. In such cases, the machine learning model may be configured to select (e.g., identify, estimate) candidate heart rate measurements, and determine a heart rate of the user based on the selected/estimated candidate heart rate measurements. For example, the machine learning model may be configured to identify PPG heart rate candidates (e.g., candidate heart rate measurements) within the received heart rate data, and estimate heart rate measurements based on the identified candidate heart rates (e.g., by selecting one of the candidate heart rate measurements, modifying the candidate heart rate measurements, estimating new candidate heart rate measurements, etc.).

As another example, the machine learning model may be configured to identify or extract time and/or frequency domain features within the received PPG data and motion data. In this example, time-domain and/or frequency-domain features may be used as inputs to a machine learning model to determine/estimate heart rate measurements. For example, the time/frequency domain features extracted from the acquired physiological data may include candidate heart rate measurements, wherein the machine learning model is configured to receive the candidate heart rate measurements as input and estimate heart rate measurements (e.g., select or modify the candidate heart rate measurements) as output.

At 345, the wearable device may output the real heart rate data. The time series of heart rate data may be displayed in an application (e.g., GU 1275). The presentation of heart rate data is further shown and described below with reference to fig. 5.

In some cases, steps 305 through 345 may be performed by a wearable device, a user device (e.g., user device 106), an application (e.g., an application of user device 106), or a combination thereof. For example, the combination of the wearable device and the user device may perform steps 305 to 345, and the application may output heart rate data on the user device. In some cases, steps 305 through 335 may be performed every n seconds (e.g., such as every second). Additionally or alternatively, one or more aspects of step 340 may be performed periodically in accordance with a configuration. For example, the wearable device may detect a strong motion signal every x seconds (e.g., 5 seconds) and a strong PPG signal every y seconds (e.g., 15 seconds). The wearable device may be configured to perform at least steps 305 through 335 during an activity, exercise, etc., and may be configured to perform one or more aspects of steps 340 and 345 after the activity, exercise, etc. In some cases, the wearable device may be configured to perform one or more aspects of steps 340 and 345 during an activity, exercise, or the like.

Fig. 4 illustrates an example of a heart rate determination procedure 400 supporting techniques for measuring heart rate in accordance with aspects of the present disclosure. Heart rate determination program 400 may implement aspects of system 100, system 200, or a combination thereof, or by aspects of system 100, system 200, or a combination thereof. In some cases, one or more aspects of heart rate determination procedure 400 may be the same as or similar to one or more aspects of heart rate determination procedure 300. In some implementations, the heart rate determination program 400 may generate heart rate data (e.g., activity heart rate data, exercise heart rate data) that may be displayed to the user via the GUI 275 of the user device 106, as shown in fig. 2.

As described herein, a system (such as system 200) or a portion of system 200 (such as a wearable device (e.g., ring 104)) may identify heart rate data of a user from a set of heart rate data, where the heart rate data may include motion artifacts. In some cases, the wearable device may detect heart rate data of the user during periods of activity, movement, and motion according to techniques described herein. In some cases, the techniques described herein for determining heart rate data may be used for heart rate detection other than activity heart rate detection, or for all heart rate detection (e.g., whether a user is active or moving). Thus, the heart rate determination procedure may not be limited to determining the heart rate of the user during exercise, sports, activities, etc., and may additionally or alternatively be used to determine the heart rate during periods of rest, relaxation, accommodation, etc.

As described with reference to fig. 3, the wearable device may obtain PPG data. To measure PPG data, the wearable device may sample PPG data of the user using one or more sets of PPG sensors, wherein each set of PPG sensors comprises at least one LED and at least one photodetector. For example, in a wearable device that includes two pairs of PPG sensors, the wearable device may obtain four PPG signals (e.g., four PPG channels). For example, the wearable device may obtain PPG graphs 405-a, 405-b, 405-c, 405-d representing PPG data obtained via four different channels (e.g., four different PPG sensors).

The wearable device and/or other components of system 200 may individually pre-process each PPG channel and may then perform a multipath combining procedure in order to obtain a single PPG signal (e.g., a complex PPG signal). In some cases, the wearable device may be configured to select one channel from a set of channels. The selection may be based on quality such that the wearable device may select the channel associated with the highest quality, the least number of outliers, noise, etc. In some cases, the wearable device may combine two or more PPG signals using one or more mathematical operations. For example, the wearable device may average at least two of the channels. In some cases, the wearable device may average the first two or three channels associated with the highest quality (e.g., associated with the least amount of outliers, noise, etc.). In some cases, the wearable device may be configured to average any number of channels if each of the channels meets a quality threshold. In some cases, the wearable device may be configured to average a particular number of channels or a particular subset of channels. The wearable device may be configured to average all four PPG channels to obtain an average PPG channel. In some cases, determining a single PPG channel may be based on one or more mathematical expressions. Thus, the output of the multipath combining module is a single PPG channel, such as PPG pattern 405-e (e.g., a synthesized PPG signal).

Further, the wearable device may measure motion data (e.g., acceleration data) associated with the user. To measure motion data, the wearable device may utilize one or more motion sensors (e.g., motion sensor 245) on the wearable device. In some cases, the wearable device may obtain motion data in the x-axis, y-axis, and z-axis, where each axis may be referred to as a different motion channel. Thus, the wearable device may obtain motion data through three motion channels. Referring to FIG. 4, graphs 410-a, 410-b, and 410-c may represent motion data obtained in the x-axis, y-axis, and z-axis, respectively. For example, graphic 410-a may be associated with motion obtained in the x-axis, graphic 410-b may be associated with motion obtained in the y-axis, and graphic 410-c may be associated with motion obtained in the z-axis. The wearable device may pre-process each channel and then determine whether to combine one or more channels to obtain a single motion channel.

In some cases, the wearable device and/or other components of the system 200 may be configured to select a motion channel from a set of channels. The selection may be based on quality such that the wearable device may select the channel associated with the highest quality, the least number of outliers, noise, etc. In some cases, the wearable device may use one or more mathematical operations to combine two or more of the motion channels. For example, the wearable device may average at least two of the channels. In some cases, the wearable device may average the two channels associated with the highest quality (e.g., associated with the least amount of outliers, noise, etc.). In some cases, the wearable device may be configured to average any of the channels if each of the channels meets a quality threshold. In some cases, the wearable device may be configured to average a particular number of channels or a particular subset of channels. The wearable device may be configured to average all three motion channels to obtain an average channel. In some cases, determining the signal motion channel may be based on one or more mathematical expressions. Thus, upon selection of a single x, y, or z channel, or upon combining two or more of the channels, the wearable device may obtain a single motion profile 410-d (e.g., a composite motion signal).

In determining the single PPG signal and the motion signal, the wearable device may determine the heart rate of the user. However, as noted previously herein, PPG data may be sensitive to motion, where in some cases, some activities or exercises may produce PPG signals that are not attributable to heart rate. As such, the PPG signal may represent a false heart rate 425 (e.g., motion artifacts). For example, referring to fig. 4, the graph 415 may depict an actual heart rate 420 and a false heart rate 425. Thus, to provide accurate heart rate data to the user, the wearable device may perform steps 320, 340, and 345 as described with reference to fig. 3 to detect the actual heart rate signal of the user. In other words, the actual heart rate 420 and the false heart rate 425 may include "candidate" heart rate measurements, and the system 200 may be configured to determine whether the PPG data corresponding to the actual heart rate 420 or the PPG data corresponding to the false heart rate 425 accurately represent the actual heart rate of the user.

Thus, the wearable device may identify one or more heart rate measurements from a set of candidate heart rate measurements as motion artifacts based at least in part on a comparison of data trends between the set of candidate heart rate measurements (e.g., actual heart rate 420, false heart rate 425) and the motion data. The wearable device may select a first heart rate measurement from a subset of a set of candidate heart rate measurements that does not include one or more heart rate measurements identified as motion artifacts. For example, the system 200 may identify the false heart rate 435 as a motion artifact, and thus may select the actual heart rate 420 from a set of candidate heart rate measurements as a heart rate reflecting the actual heart rate of the user during a given time interval.

Identifying one or more heart rate measurements as motion artifacts may include: one or more heart rate measurements from a set of candidate heart rate measurements are identified as motion artifacts based at least in part on the one or more heart rate measurements exhibiting a similar frequency pattern relative to the motion data. As previously noted herein, the system 200 may use motion data (such as combined motion data, motion intensity data, intensity change rate data, or a combination thereof) to determine which of the candidate heart rate measurement/PPG signals are due to motion and which heart rate measurement/PPG signal represents the actual heart rate of the user. In some cases, the wearable device may analyze the motion data and PPG data by unit so that the wearable device may determine at which data points the other PPG signals overlap with the motion signal. The wearable device may remove PPG signals that overlap with or are otherwise attributable to motion (e.g., remove motion artifacts).

As previously noted herein, in some implementations, the system 200 may utilize a machine learning model/algorithm (e.g., heuristic-based model, deep learning model, regression-based model) to perform heart rate detection. In particular, the machine learning model may be trained to receive PPG data and motion data from the wearable device and output heart rate measurements/estimates based on the received data. For example, time-domain/frequency-domain PPG data (e.g., data including PPG heart rate candidates/candidate heart rates) and time-domain/frequency-domain motion data may be input into a machine learning model, where the machine learning model is configured to estimate/determine heart rate measurements based on the received time-domain/frequency-domain PPG and motion data.

Fig. 5 illustrates an example of a GUI 500 supporting techniques for measuring heart rate in accordance with various aspects of the disclosure. GUI 500 may implement aspects of system 100, system 200, heart rate determination process 300, heart rate determination process 400, or any combination thereof, or by aspects of system 100, system 200, heart rate determination process 300, heart rate determination process 400, or any combination thereof. For example, GUI 500 may include an example of GUI 275 included within user device 106 shown in fig. 2.

GUI 500 illustrates a series of application pages 505 that may be displayed to a user via GUI 500 (e.g., GUI 275 shown in fig. 2). The server 110 of the system 200 may cause the GU1 500 of the user device 106 (e.g., mobile device) to display an indication of heart rate data (e.g., via the application page 505-a or 505-b). Thus, upon determining heart rate data (e.g., as described with reference to fig. 3 and 4), upon opening the wearable application 250, the user may be presented with the application page 505-a. As shown in FIG. 5, the application page 505-a may display a heart rate map 510-a. Heart rate map 510-a may include a visual representation of how a user's heart rate reacts to different events and activities (e.g., exercise, sleep, rest, etc.).

In some cases, heart rate map 510-a may display the heart rate of the user over minutes, hours, days, etc. In some cases, the heart rate map 510 may display a combination of daytime and nighttime heart rate data (e.g., awake and sleep heart rate data). Additionally, in some implementations, the application page 505-a can display one or more scores (e.g., sleep score, readiness score 515, activity score, inactivity time) for the user on respective days (e.g., respective sleep days), where the one or more scores can be based on heart rate data. As another example, heart rate data may be used to update at least a subset of the factors of the readiness score 515 (e.g., sleep subset, sleep balance, HRV balance, recovery index, activity balance). In some cases, the application page 505-a may include an add button 530 that the user may press to add additional information to the page, such as workouts, time of day, tags, etc.

With continued reference to FIG. 5, the user can select a heart rate graph 510-a on application page 505-a to view details associated with heart rate, as shown in application page 505-b ("heart rate modality"). In other words, a tap on the heart rate graph 510-a shown on the application page 505-a may cause the GUI 500 to display the application page 505-b so that the user may quickly and easily view the user's heart rate over time. The application page 505-b may include a modality view that includes details of heart rate. Heart rate graphs 510-a and 510-b may display the same or different graphs. For example, the time scales may be the same or different. In some cases, the application page 505-b may indicate a portion of the heart rate map 510-b associated with increased exercise, activity, etc., such as to explain the cause of heart rate changes. The application page 505-b may also include a daytime heart rate range 520-a, a relaxation heart rate range 520-b, a sleep heart rate range 520-c, an athletic heart rate range 520-d, and the like. For example, the ranges may be on an hourly basis. In some cases, the application page 505-b may display heart rate data as HRV, resting heart rate, and the like.

In some cases, the user may be able to select a range of motion 520-d, or some other aspect of the application page 505-b associated with an exercise, movement, activity, etc., in order to view details associated with heart rate during such activity, as shown in application page 505-c ("exercise heart rate modality"). In other words, tapping on an aspect of the application page 505-b may cause the GUI 500 to display the application page 505-c such that the user may quickly and easily view the user's heart rate over time during a particular activity, such as the last motion performed by the user. The application page 505-c may include a modality view that includes details of heart rate. In some cases, the application page 505-c may display an athletic heart rate map 510-c to indicate how the user's rate changes during exercise, activity, etc. In some cases, the duration of the heart rate graph 510-c may be based on input from the user. For example, the user may add motion (e.g., using button 530), wherein the user may instruct or the wearable device and/or application may additionally determine a length of the workout, wherein heart rate map 510-c may be based on the length. The application page 505-c may also include an exercise heart rate range 525-a, a maximum exercise heart rate range 525-b, a minimum exercise heart rate 525-c, an average exercise heart rate 525-d, and so on. For example, the ranges may be on a per exercise basis.

In some implementations, the system 200 may be configured to determine one or more accuracy metrics (e.g., a "signal check" metric) for the determined heart rate measurements. The accuracy metric may include any metric or predictor (predictor) of how accurate and/or reliable the determined heart rate measurement is for the user. In some aspects, the accuracy metric may be determined or derived from the intensity/power of the candidate heart rate measurements and the amount of strong candidate heart rate measurements over some measurement period or interval. In the case of low signal quality (e.g., due to too loose a ring, too strong motion artifacts, etc.), the system 200 may not be able to measure the user's heart rate with certainty, which may result in a relatively low accuracy metric (e.g., relatively low accuracy/reliability of the heart rate measurement). In some implementations, the system 200 may be configured to discard, ignore, or otherwise avoid displaying heart rate measurements with accuracy metrics less than a certain threshold metric (to avoid presenting false heart rate values to the user). In other words, the system 200 may be configured to display only heart rate measurements exhibiting a certain threshold level of accuracy/reliability (e.g., heart rate measurements having an accuracy metric that meets a certain threshold metric).

The server of the system may cause the GUI 500 of the user device to display a message on the application page 505-a, 505-b, 505-c, or a combination thereof, associated with the identified heart rate data. The user device may display the recommendation and/or information associated with the heart rate data via a message. In some implementations, the user device 106 and/or the server 110 may generate an alert (e.g., message, insight) associated with the heart rate data, which may be displayed to the user via the GUI 500 (e.g., application pages 505-a, 505-b, 50-c, or some other application page). In particular, messages generated via GUI 500 and displayed to the user may be associated with one or more characteristics of the heart rate data (e.g., time of day, duration, range). For example, the message may alert the user to breathe, relax from time to time, etc., based on the user's heart rate. In some cases, the message may alert the user to perform an exercise or activity because it has passed a duration greater than a threshold since the user last performed the activity or exercise. In some cases, the message may display to the user a recommendation of how to adjust their lifestyle to achieve a particular heart rate. For example, the message may alert the user to target a particular heart rate during the workout, where the recommendation may be based on the type of workout the user is performing. In some cases, the message may alert the user to decrease exercise intensity to decrease the user's heart rate, or increase intensity to reach a target heart rate, etc. In this regard, the system may be configured to display messages or insights to the user in order to facilitate an efficient, healthy mode for the user.

Heart rate map 510 may be illustrated via any visual representation, including a line graph, a bar graph, or any combination thereof. For example, heart rate graphs 510-a and 510-b may include line graphs, where heart rate graph 510-c may include line graphs that overlap (or are above) heart rate measurement 535 (e.g., heart rate measurement). In some implementations, heart rate measurements 535 (such as heart rate measurements 535-a and 535-b) may account for a series of potential heart rate measurements over a given period of time. In this regard, the relative height of the heart rate measurements may be associated with (or indicate) the relative confidence measure of the respective heart rate measurement 535. For example, as shown in fig. 5, the first heart rate measurement 535-a exhibits a greater height than the second heart rate measurement 535-b, which may indicate that the system 200 is able to calculate the second heart rate measurement 535-b with a higher degree of confidence (e.g., more accurate calculation) than the first heart rate measurement 535-a.

In some implementations, the user device 106 may be configured to show or otherwise indicate the heart rate measurement 535 associated with the heart rate map 510 via tactile feedback, audio feedback, or the like. In particular, the user device 106 may be configured to provide haptic and/or audio feedback corresponding to different heart rate measurements (e.g., heart rate measurement 535) when the user interacts (e.g., touches, presses) with different portions of the heart rate map 510.

For example, the user may slide their finger (or mouse, stylus, etc.) along the heart rate map 510-c to select different heart rate measurements 535 within the heart rate map 510-c. In this example, the user device 106 may provide haptic feedback (or audio feedback) indicative of the selected heart rate measurement 535. For example, where the first heart rate measurement 535-a is 70 beats per minute, when the user selects (e.g., presses and holds, clicks, etc.) the first heart rate measurement 535-a (or a point on the heart rate map 510-c corresponding to a point along the x-axis that corresponds to the point associated with the first heart rate measurement 535-a), the user device 106 may provide haptic and/or audio feedback at a rate of 70 vibrations, beeps, sounds, etc. per minute. Similarly, where the second heart rate measurement 535-b is 85 beats per minute, the user device 106 may provide tactile and/or audio feedback at a rate of 85 vibrations, beeps, sounds, etc. per minute when the user selects (e.g., presses and holds, clicks, etc.) the second heart rate measurement 535-b (or a point on the heart rate map 510-c corresponding to a point along the x-axis corresponding to the second heart rate measurement 535-b).

Fig. 6 illustrates a block diagram 600 of a device 605 that supports techniques for measuring heart rate in accordance with various aspects of the disclosure. The device 605 may include an input module 610, an output module 615, and a wearable application 620. The device 605 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

The input module 610 may provide a means for receiving information (e.g., packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to disease detection techniques). Information may be passed to other components of the device 605. The input module 610 may utilize a single antenna or a set of multiple antennas.

The output module 615 may provide a means for transmitting signals generated by other components of the device 605. For example, the output module 615 may transmit information associated with various information channels (e.g., control channels, data channels, information channels related to disease detection techniques), such as packets, user data, control information, or any combination thereof. In some examples, the output module 615 may be co-located with the input module 610 in the transceiver module. The output module 615 may use a single antenna or a set of multiple antennas.

For example, the wearable application 620 may include a physiological data component 625, a candidate heart rate determination component 630, a heart rate selection component 635, a heart rate determination component 640, or any combination thereof. In some examples, the wearable application 620 or various components thereof may be configured to perform various operations (e.g., receive, monitor, transmit) using or otherwise in cooperation with the input module 610, the output module 615, or both. For example, the wearable application 620 may receive information from the input module 610, send information to the output module 615, or be integrated with the input module 610, the output module 615, or both to receive information, send information, or perform various other operations described herein.

The physiological data component 625 may be configured or otherwise support means for receiving physiological data associated with a user, including PPG data and motion data collected over a first time interval via a wearable device associated with the user. The candidate heart rate determination component 630 may be configured or otherwise support means for determining a set of candidate heart rate measurements within a first time interval based at least in part on PPG data. The heart rate selection component 635 may be configured or otherwise support means for selecting a first heart rate measurement from a set of candidate heart rate measurements based at least in part on the received motion data. The heart rate determination component 640 may be configured or otherwise support means for determining a first heart rate of the user over a first time interval based at least in part on the selected first heart rate measurement.

Fig. 7 illustrates a block diagram 700 of a wearable application 720 supporting techniques for measuring heart rate in accordance with various aspects of the disclosure. Wearable application 720 may be an example of an aspect of wearable application or wearable application 620, or both, as described herein. Wearable application 720, or various components thereof, may be an example of an apparatus for performing various aspects of the techniques for measuring heart rate as described herein. For example, wearable application 720 may include physiological data component 725, candidate heart rate determination component 730, heart rate selection component 735, heart rate determination component 740, motion artifact identification component 745, PPG signal combination component 750, acceleration data combination component 755, data display component 760, heart rate interpolation component 765, or any combination thereof. Each of these components may communicate with each other directly or indirectly (e.g., via one or more buses).

The physiological data component 725 may be configured or otherwise support means for receiving physiological data associated with the user, including PPG data and motion data collected over a first time interval via a wearable device associated with the user. The candidate heart rate determination component 730 may be configured or otherwise support means for determining a set of candidate heart rate measurements within a first time interval based at least in part on PPG data. The heart rate selection component 735 may be configured or otherwise support means for selecting a first heart rate measurement from a set of candidate heart rate measurements based at least in part on the received motion data. The heart rate determination component 740 may be configured or otherwise support means for determining a first heart rate of the user over a first time interval based at least in part on the selected first heart rate measurement.

In some examples, the motion artifact identification component 745 may be configured or otherwise enabled to identify one or more heart rate measurements from a set of candidate heart rate measurements as motion artifacts based at least in part on a comparison of data trends between the set of candidate heart rate measurements and the motion data. In some examples, heart rate selection component 735 may be configured or otherwise support means for selecting a first heart rate measurement from a subset of the set of candidate heart rate measurements that does not include one or more heart rate measurements identified as motion artifacts.

In some examples, to support identifying one or more heart rate measurements as motion artifacts, the motion artifact identification component 745 may be configured or otherwise support means for identifying one or more heart rate measurements from a set of candidate heart rate measurements as motion artifacts based at least in part on the one or more heart rate measurements exhibiting a similar frequency pattern relative to the motion data.

In some examples, the physiological data component 725 may be configured or otherwise support means for receiving additional physiological data associated with the user, the additional physiological data including additional PPG data and additional motion data collected via the wearable device throughout a second time interval, the second time interval subsequent to the first time interval. In some examples, candidate heart rate determination component 730 may be configured or otherwise support means for determining an additional set of candidate heart rate measurements within the second time interval based at least in part on the additional PPG data. In some examples, heart rate selection component 735 may be configured or otherwise support means for selecting a second heart rate measurement from an additional set of candidate heart rate measurements based at least in part on the received additional motion data. In some examples, the heart rate determination component 740 may be configured or otherwise support means for determining a second heart rate of the user within the second time interval based at least in part on the selected second heart rate measurement.

In some examples, heart rate interpolation component 765 may be configured or otherwise support means for interpolating between a first heart rate measurement for a first time interval and a second heart rate measurement for a second time interval, wherein determining the first heart rate of the user within the first time interval, determining the second heart rate of the user within the second time interval, or both are based at least in part on the interpolation.

In some examples, to support interpolation, heart rate interpolation component 765 may be configured or otherwise support means for interpolating between a first heart rate measurement of a first time interval and a second heart rate measurement of a second time interval based at least in part on the intensity of motion data collected over the first time interval, the intensity of additional motion data collected over the second time interval, or both.

In some examples, the PPG data includes a plurality of PPG signals acquired from a plurality of pairs of PPG sensors, and PPG signal combining component 750 may be configured or otherwise support means for combining the plurality of PPG signals to generate a composite PPG signal using one or more mathematical operations including an averaging operation, a weighted averaging operation, or both, wherein determining the set of heart rate data sequences is based at least in part on the composite PPG signal.

In some examples, the motion data includes first acceleration data relative to a first direction, and the acceleration data combining component 755 may be configured or otherwise enabled to combine first acceleration data, second acceleration data, third acceleration data, or any combination thereof using one or more mathematical operations based at least in part on one or more characteristics of the first acceleration data, the second acceleration data, or the third acceleration data, wherein selecting the first heart rate data sequence from the set of heart rate data sequences is based at least in part on combining the first acceleration data, the second acceleration data, the third acceleration data, or any combination thereof.

In some examples, to support determining a set of candidate heart rate measurements, candidate heart rate determination component 730 may be configured or otherwise support means for determining a set of candidate heart rate measurements over a frequency range corresponding to an expected range of human heart rates.

In some examples, the data display component 760 may be configured or otherwise support means for causing a GUI of a user device associated with a user to display an indication of the first heart rate.

In some examples, the wearable device includes a wearable ring device.

In some examples, the wearable device collects physiological data from the user based on arterial blood flow.

Fig. 8 illustrates a schematic diagram of a system 800 including a device 805 that supports techniques for measuring heart rate in accordance with various aspects of the present disclosure. The device 805 may be an example of a component of the device 605 as described herein or include a component of the device 605 as described herein. Device 805 may include an example of user device 106, as previously described herein. The device 805 may include components for two-way communication including components for sending and receiving communications with the wearable device 104 and the server 110, such as a wearable application 820, a communication module 810, an antenna 815, a user interface component 825, a database (application data) 830, a memory 835, and a processor 840. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 845).

The communication module 810 may manage input and output signals for the device 805 via an antenna 815. The communication module 810 may include an example of the communication module 220-b of the user device 106 shown and described in fig. 2. In this regard, the communication module 810 may manage communication with the ring 104 and the server 110, as shown in fig. 2. The communication module 810 may also manage peripheral devices not integrated into the device 805. In some cases, the communication module 810 may represent a physical connection or port to an external peripheral device. In some cases, communication module 810 may utilize an operating system, such asMS-MS- Or other known operating systems. In other cases, communication module 810 may represent or interact with a wearable device (e.g., ring 104), a modem, a keyboard, a mouse, a touch screen, or the like. In some cases, communication module 810 may be implemented as part of processor 840. In some examples, a user may interact with device 805 via communication module 810, user interface component 825, or via hardware components controlled by communication module 810.

In some cases, device 805 may include a single antenna 815. However, in some other cases, the apparatus 805 may have more than one antenna 815, which antenna 815 may be capable of transmitting or receiving multiple wireless transmissions simultaneously. The communication module 810 may communicate bi-directionally via one or more antennas 815, wired links, or wireless links as described herein. For example, communication module 810 may represent a wireless transceiver and may bi-directionally communicate with another wireless transceiver. The communication module 810 may also include a modem for modulating data packets, providing the modulated data packets to one or more antennas 815 for transmission, and demodulating data packets received from the one or more antennas 815.

The user interface component 825 can manage data storage and processing in the database 830. In some cases, a user may interact with user interface component 825. In other cases, the user interface component 825 may operate automatically without user interaction. Database 830 may be an example of a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.

The memory 835 may include RAM and ROM. The memory 835 may store computer readable, computer executable software comprising instructions that, when executed, cause the processor 840 to perform the various functions described herein. In some cases, memory 835 may contain a basic I/O system (BIOS) or the like, which may control basic hardware or software operations, such as interactions with peripheral components or devices.

Processor 840 may include intelligent hardware devices (e.g., general purpose processors, digital Signal Processors (DSPs), central Processing Units (CPUs), microcontrollers, application Specific Integrated Circuits (ASICs), programmable gate arrays (FPGAs), programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combinations thereof). In some cases, processor 840 may be configured to operate a memory array using a memory controller. In other cases, the memory controller may be integrated into the processor 840. Processor 840 may be configured to execute computer readable instructions stored in memory 835 to perform various functions (e.g., functions or tasks supporting methods and systems for sleep phasing algorithms).

For example, wearable application 820 may be configured or otherwise support means for receiving physiological data associated with a user, including PPG data and motion data collected over a first time interval via a wearable device associated with the user. Wearable application 820 may be configured or otherwise support means for determining a set of candidate heart rate measurements within a first time interval based at least in part on PPG data. Wearable application 820 may be configured or otherwise support means for selecting a first heart rate measurement from a set of candidate heart rate measurements based at least in part on the received motion data. Wearable application 820 may be configured or otherwise support means for determining a first heart rate of the user over a first time interval based at least in part on the selected first heart rate measurement.

According to examples as described herein, by including or configuring wearable application 820, device 805 may support techniques for improved heart rate data for a user and improved alerts or instructions provided to the user.

Wearable application 820 may include an application program (e.g., "application"), program, software, or other component configured to facilitate communication with finger ring 104, server 110, other user device 106, etc. For example, wearable application 820 may include an application executable on user device 106, user device 106 configured to receive data (e.g., physiological data) from finger ring 104, perform processing operations on the received data, send and receive data with server 110, and cause presentation of the data to user 102.

Fig. 9 illustrates a flow chart of a method 900 supporting techniques for measuring heart rate in accordance with various aspects of the disclosure. The operations of method 900 may be implemented by a user device or components thereof as described herein. For example, the operations of method 900 may be performed by a user device as described with reference to fig. 1-8. In some examples, a user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

At 905, the method may include: physiological data associated with a user is received, the physiological data including PPG data and motion data collected over a first time interval via a wearable device associated with the user. The operations of 905 may be performed in accordance with examples as disclosed herein. In some examples, various aspects of the operation of 905 can be performed by the physiological data component 725 as described with reference to fig. 7.

At 910, the method may include: a set of candidate heart rate measurements within a first time interval is determined based at least in part on PPG data. The operations of 910 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 910 may be performed by candidate heart rate determination component 730 as described with reference to fig. 7.

At 915, the method may include: a first heart rate measurement is selected from the set of candidate heart rate measurements based at least in part on the received motion data. 915 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 915 may be performed by the heart rate selection component 735 as described with reference to fig. 7.

At 920, the method may include: a first heart rate of the user over a first time interval is determined based at least in part on the selected first heart rate measurement. The operations of 920 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 920 may be performed by the heart rate determination component 740 as described with reference to fig. 7.

Fig. 10 illustrates a flow chart showing a method 1000 supporting techniques for measuring heart rate in accordance with various aspects of the present disclosure. The operations of method 1000 may be implemented by a user device or component thereof as described herein. For example, the operations of method 1000 may be performed by a user device, as described with reference to fig. 1-8. In some examples, a user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

At 1005, the method may include: physiological data associated with a user is received, the physiological data including PPG data and motion data collected over a first time interval via a wearable device associated with the user. Operations of 1005 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1005 can be performed by the physiological data component 725 as described with reference to fig. 7.

At 1010, the method may include: the plurality of PPG signals are combined using one or more mathematical operations to generate a composite PPG signal, the one or more mathematical operations including an average operation, a weighted average operation, or both. The operations of 1010 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1010 may be performed by PPG signal combining component 750 as described with reference to fig. 7.

At 1015, the method may include: a set of candidate heart rate measurements within the first time interval is determined based at least in part on the PPG data, wherein determining the set of candidate heart rate measurements is based at least in part on the composite PPG signal. 1015 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1015 may be performed by candidate heart rate determination component 730 as described with reference to fig. 7.

At 1020, the method may include: a first heart rate measurement is selected from the set of candidate heart rate measurements based at least in part on the received motion data. Operations of 1020 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1020 may be performed by heart rate selection component 735 as described with reference to fig. 7.

At 1025, the method may include: a first heart rate of the user over the first time interval is determined based at least in part on the selected first heart rate measurement. The operations of 1025 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1025 may be performed by heart rate determination component 740 as described with reference to fig. 7.

Fig. 11 illustrates a flow chart showing a method 1100 supporting techniques for measuring heart rate in accordance with various aspects of the disclosure. The operations of method 1100 may be implemented by a user device or component thereof as described herein. For example, the operations of method 1100 may be performed by a user device as described with reference to fig. 1-8. In some examples, a user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

At 1105, the method may include: physiological data associated with a user is received, the physiological data including PPG data and motion data collected over a first time interval via a wearable device associated with the user. The operations of 1105 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1105 may be performed by the physiological data component 725, as described with reference to fig. 7.

At 1110, the method may include: a set of candidate heart rate measurements within a first time interval is determined based at least in part on PPG data. Operations of 1110 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1110 may be performed by candidate heart rate determination component 730 as described with reference to fig. 7.

At 1115, the method may include: a first heart rate measurement is selected from the set of candidate heart rate measurements based at least in part on the received motion data. The operation of 1115 may be performed according to examples as disclosed herein. In some examples, aspects of the operation of 1115 may be performed by the heart rate selection component 735 as described with reference to fig. 7.

At 1120, the method may include: a first heart rate of the user over the first time interval is determined based at least in part on the selected first heart rate measurement. The operations of 1120 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1120 may be performed by heart rate determination component 740 as described with reference to fig. 7.

At 1125, the method may include: causing a GUI of a user device associated with the user to display an indication of the first heart rate. The operations of 1125 may be performed according to examples as disclosed herein. In some examples, various aspects of the operation of 1125 may be performed by data display component 760 as described with reference to fig. 7.

It should be noted that the above-described methods describe possible implementations, and that these operations and steps may be rearranged or otherwise modified, and that other implementations are possible. Further, aspects from two or more of these methods may be combined.

A method is described. The method may include: the method includes receiving physiological data associated with a user, the physiological data including PPG data and motion data collected over an entire first time interval via a wearable device associated with the user, determining a set of candidate heart rate measurements over the first time interval based at least in part on the PPG data, selecting a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data, and determining a first heart rate of the user over the first time interval based at least in part on the selected first heart rate measurement.

An apparatus is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to receive physiological data associated with the user, the physiological data including PPG data and motion data collected over a first time interval via a wearable device associated with the user, determine a set of candidate heart rate measurements over the first time interval based at least in part on the PPG data, select a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data, and determine a first heart rate of the user over the first time interval based at least in part on the selected first heart rate measurement.

Another apparatus is described. The apparatus may include: the apparatus includes means for receiving physiological data associated with the user, the physiological data including PPG data and motion data collected over an entire first time interval via a wearable device associated with the user, means for determining a set of candidate heart rate measurements over the first time interval based at least in part on the PPG data, means for selecting a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data, and means for determining a first heart rate of the user over the first time interval based at least in part on the selected first heart rate measurement.

A non-transitory computer readable medium storing code is described. The code may include instructions executable by a processor for receiving physiological data associated with the user, the physiological data including PPG data and motion data collected over a first time interval via a wearable device associated with the user, determining a set of candidate heart rate measurements over the first time interval based at least in part on the PPG data, selecting a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data, and determining a first heart rate of the user over the first time interval based at least in part on the selected first heart rate measurement.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, apparatus, or instructions to: the method may include identifying one or more heart rate measurements from a set of candidate heart rate measurements as motion artifacts based at least in part on a comparison of data trends between the set of candidate heart rate measurements and the motion data, and selecting a first heart rate measurement from a subset of the set of candidate heart rate measurements that does not include the one or more heart rate measurements identified as motion artifacts.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, identifying the one or more heart rate measurements as motion artifacts may include operations, features, means, or instructions to identify the one or more heart rate measurements from the set of candidate heart rate measurements as motion artifacts based at least in part on the one or more heart rate measurements exhibiting a similar frequency pattern relative to the motion data.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, apparatus, or instructions to: receiving additional physiological data associated with the user, the additional physiological data including additional PPG data and additional motion data collected via the wearable device throughout a second time interval, the second time interval subsequent to the first time interval, determining an additional set of candidate heart rate measurements within the second time interval based at least in part on the additional PPG data, selecting a second heart rate measurement from the additional set of candidate heart rate measurements based at least in part on the received additional motion data, and determining a second heart rate of the user within the second time interval based at least in part on the selected second heart rate measurement.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, apparatus, or instructions to: interpolation is performed between the first heart rate measurement for the first time interval and the second heart rate measurement for the second time interval, wherein determining the first heart rate of the user within the first time interval, determining the second heart rate of the user within the second time interval, or both may be based at least in part on the interpolation.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the interpolation may include operations, features, means, or instructions to interpolate between the first heart rate measurement for the first time interval and the second heart rate measurement for the second time interval based at least in part on the intensity of the motion data collected throughout the first time interval, the intensity of the additional motion data collected throughout the second time interval, or both.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the PPG data includes a plurality of PPG signals acquired from a plurality of pairs of PPG sensors, the methods, apparatus, and non-transitory computer-readable media may further include operations, features, apparatus, or instructions to: the plurality of PPG signals are combined to generate a composite PPG signal using one or more mathematical operations including an averaging operation, a weighted averaging operation, or both, wherein determining the set of heart rate data sequences may be based at least in part on the composite PPG signal.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the motion data includes first acceleration data relative to a first direction, and the methods, apparatus, and non-transitory computer-readable media may include further operations, features, apparatus, or instructions to: combining first acceleration data, second acceleration data, third acceleration data, or any combination thereof using one or more mathematical operations based at least in part on one or more characteristics of the first acceleration data, the second acceleration data, or the third acceleration data, wherein selecting the first sequence of heart rate data from the set of sequences of heart rate data may be based at least in part on combining the first acceleration data, the second acceleration data, the third acceleration data, or any combination thereof.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, determining the set of candidate heart rate measurements may include operations, features, apparatus, or instructions for determining the set of candidate heart rate measurements over a frequency range corresponding to an expected range of human heart rates.

Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may further include operations, features, means, or instructions for causing a GUI of a user device associated with the user to display an indication of the first heart rate.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device comprises a wearable ring device.

In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device collects physiological data from a user based on arterial blood flow.

The description set forth herein in connection with the appended drawings describes example configurations and is not intended to represent all examples that may be implemented or within the scope of the claims. The term "exemplary" as used herein means "serving as an example, instance, or illustration," rather than "preferred" or "advantageous over other examples. The detailed description includes specific details for the purpose of providing an understanding of the described technology. However, these techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples.

In the drawings, similar components or features may have the same reference numerals. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description applies to any one of the similar components having the same first reference label without regard to the second reference label.

Any of a number of different techniques and means may be used to represent the information and signals described herein. For example, data, instructions, commands, information, signals, bits, symbols, and chips (chips) may be referenced throughout the above description, may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or performed with a general purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software for execution by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the present disclosure and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or a combination of any of these. Features that implement the functions may also be physically located at various locations including being distributed such that portions of the functions are implemented at different physical locations. Furthermore, as used herein, including in the claims, an "or" as used in a list of items (e.g., a list of items starting with a phrase such as "at least one of" or "one or more of" indicates an inclusive list such that, for example, a list of at least one of A, B or C means a or B or C, or AB or AC or BC, or ABC (i.e., a and B and C). Moreover, as used herein, the phrase "based on" should not be construed as referring to a set of closed conditions. For example, exemplary steps described as "based on condition a" may be based on both condition a and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on".

Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. Non-transitory storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, non-transitory computer-readable media can comprise RAM, ROM, electrically Erasable Programmable ROM (EEPROM), compact Disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Further, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes CD, laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.

The description herein is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring heart rate of a user, comprising:

Receiving physiological data associated with the user, the physiological data including photoplethysmogram (PPG) data and motion data collected over a first time interval via a wearable device associated with the user;

determining a set of candidate heart rate measurements within the first time interval based at least in part on the PPG data;

Selecting a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data; and

A first heart rate of the user within the first time interval is determined based at least in part on the selected first heart rate measurement.

2. The method of claim 1, further comprising:

The PPG data and the motion data are input into a machine learning model, wherein selecting the first heart rate measurement, determining the first heart rate, or both are based at least in part on inputting the PPG data and the motion data into the machine learning model.

3. The method of claim 1, further comprising:

identifying one or more heart rate measurements from the set of candidate heart rate measurements as motion artifacts based at least in part on a comparison of data trends between the set of candidate heart rate measurements and the motion data; and

The first heart rate measurement is selected from a subset of the set of candidate heart rate measurements that does not include the one or more heart rate measurements identified as motion artifacts.

4. The method of claim 3, wherein identifying the one or more heart rate measurements as motion artifacts comprises:

based at least in part on the one or more heart rate measurements exhibiting a similar frequency pattern relative to the motion data, the one or more heart rate measurements from the set of candidate heart rate measurements are identified as motion artifacts.

5. The method of claim 1, further comprising:

receiving additional physiological data associated with the user, the additional physiological data including additional PPG data and additional motion data collected via the wearable device throughout a second time interval, the second time interval subsequent to the first time interval;

determining an additional set of candidate heart rate measurements within the second time interval based at least in part on the additional PPG data;

selecting a second heart rate measurement from the additional set of candidate heart rate measurements based at least in part on the received additional motion data; and

A second heart rate of the user within the second time interval is determined based at least in part on the selected second heart rate measurement.

6. The method of claim 5, further comprising:

Interpolation is performed between the first heart rate measurement for the first time interval and the second heart rate measurement for the second time interval, wherein determining the first heart rate of the user over the first time interval, determining the second heart rate of the user over the second time interval, or both are based at least in part on the interpolation.

7. The method of claim 5, wherein the interpolating comprises:

Interpolation is performed between the first heart rate measurement for the first time interval and the second heart rate measurement for the second time interval based at least in part on the intensity of the motion data collected throughout the first time interval, the intensity of the additional motion data collected throughout the second time interval, or both.

8. The method of claim 1, wherein the PPG data comprises a plurality of PPG signals acquired from a plurality of pairs of PPG sensors, wherein each pair of PPG sensors comprises at least one light emitting diode and at least one photodetector, the method further comprising:

The plurality of PPG signals are combined using one or more mathematical operations to generate a composite PPG signal, the one or more mathematical operations including an averaging operation, a weighted averaging operation, or both, wherein determining the set of candidate heart rate measurements is based at least in part on the composite PPG signal.

9. The method of claim 1, wherein the motion data includes first acceleration data about a first direction, second acceleration data about a second direction, and third acceleration data about a third direction, the method further comprising:

One or more mathematical operations are used on the first acceleration data, the second acceleration data, the third acceleration data, or any combination thereof based at least in part on one or more characteristics of the first acceleration data, the second acceleration data, or the third acceleration data, wherein selecting the first heart rate measurement from the set of candidate heart rate measurements is based at least in part on the first acceleration data, the second acceleration data, the third acceleration data, or any combination thereof.

10. The method of claim 1, wherein determining the set of candidate heart rate measurements comprises:

the set of candidate heart rate measurements is determined over a frequency range corresponding to an expected range of human heart rates.

11. The method of claim 1, further comprising:

causing a graphical user interface of a user device associated with the user to display an indication of the first heart rate.

12. The method of claim 1, wherein the wearable device comprises a wearable ring device.

13. The method of claim 1, wherein the wearable device collects the physiological data from the user based on arterial blood flow.

14. An apparatus for measuring a heart rate of a user, comprising:

A processor;

a memory coupled with the processor; and

Instructions stored in the memory and executable by the processor to cause the device to:

15. The apparatus of claim 14, wherein the instructions are further executable by the processor to cause the apparatus to:

16. The apparatus of claim 15, wherein the instructions for identifying the one or more heart rate measurements as motion artifacts are executable by the processor to cause the apparatus to:

17. The apparatus of claim 14, wherein the instructions are further executable by the processor to cause the apparatus to:

18. The apparatus of claim 17, wherein the instructions are further executable by the processor to cause the apparatus to:

19. The apparatus of claim 17, wherein the instructions for interpolating are executable by the processor to cause the apparatus to:

20. The apparatus of claim 14, wherein the PPG data comprises a plurality of PPG signals acquired from a plurality of pairs of PPG sensors, and the instructions are further executable by the processor to cause the apparatus to: