TWI891564B - Device and method for analyzing yoga movements and physiological conditions to generate practice plan - Google Patents

Abstract

A device for analyzing yoga movements and physiological conditions to generate a practice plan and a method thereof are provided. By analyzing videos of a user to generate initial posture data of the user, determining an initial physical state of the user based on the initial posture data and physiological state data of the user collected simultaneously with the videos, selecting yoga movements that are consistent with the initial physical state to generate a practice plan, and using a reality technology to simulate and display the virtual coach to demonstrate the yoga movements and explain key points of the yoga movements based on the demonstration data of the yoga movements in the practice plan, the device and the method can providing real-time action guidance to allow users to confirm whether postures of yoga movements are correct, and can achieve the effect of providing personal practice plans.

Description

Device and method for analyzing yoga movements and physiological conditions to generate practice plans

A device and method for generating a yoga exercise plan, particularly a device and method for analyzing yoga movements and physiological conditions to generate an exercise plan.

Yoga is a system that helps people realize their full potential by elevating consciousness. Yoga postures utilize ancient yet accessible techniques to improve physical, mental, emotional, and spiritual abilities. It is a form of exercise that promotes harmony between body, mind, and spirit. What we now call yoga primarily encompasses a series of methods for self-cultivation and spiritual well-being, including asanas for physical conditioning, breathing techniques for pranayama, and meditation for spiritual well-being.

Most current yoga classes provide instruction in asanas (yoga movements), that is, they provide guidance on various asanas. Therefore, in addition to taking classes in person with yoga instructors in yoga classrooms, existing yoga classes can also be taught through video teaching, and there are even auxiliary tools for yoga exercises.

However, most existing yoga training tools only provide static posture guidance, without a coach to provide guidance based on the user's actual movements. As a result, users cannot be sure whether their postures are correct and may not achieve effective training results.

In summary, it can be seen that the prior art has long had the problem of providing yoga exercise assistive tools to users to confirm whether their postures are correct. Therefore, it is necessary to propose improved technical means to solve this problem.

In view of the problem that prior art yoga exercise assistive tools provide users with confirmation of whether their postures are correct, the present invention discloses a device and method for analyzing yoga movements and physiological conditions to generate a practice plan, wherein:

The device disclosed in the present invention for analyzing yoga movements and physiological conditions to generate a practice plan includes at least: a data acquisition module for acquiring user images and physiological status data collected synchronously with the user images; an image analysis module for analyzing the user images to generate initial posture data, the initial posture data including the positions and angles of multiple key parts of the user's body, and the initial posture data corresponding to the physiological status data; a state judgment module for judging the user's posture based on the initial posture data and the physiological status data. The system uses a program to determine the user's initial physical state; a plan generation module to select multiple yoga poses that match the initial physical state to generate a practice plan. The practice plan includes yoga poses and the corresponding practice time for each yoga pose; a data loading module to load demonstration movement data corresponding to the current yoga pose; and a reality interaction module to use reality technology to simulate a virtual coach and use reality technology to simulate and display the virtual coach performing the current yoga pose based on the demonstration movement data and explain the key points of the movement.

The method disclosed in the present invention for analyzing yoga movements and physiological conditions to generate a practice plan includes at least the following steps: obtaining a user image and physiological status data collected synchronously with the user image; analyzing the user image to generate initial posture data, the initial posture data including the positions and angles of multiple key parts of the user's body, and the initial posture data corresponding to the physiological status data; and performing a training program based on the initial posture data and the physiological status data. The system uses data to determine the user's initial physical condition; selects multiple yoga poses that match the initial physical condition to generate a practice plan, which includes yoga poses and the corresponding practice time for each yoga pose; uses real-world technology to simulate a virtual coach; loads demonstration movement data corresponding to the current yoga pose; and uses real-world technology to simulate and display a virtual coach performing the current yoga pose based on the demonstration movement data and explaining the key points of the movement.

The device and method disclosed in the present invention, as described above, differ from prior art in that the present invention generates initial posture data of the user by analyzing user images. Based on the user's initial posture data and physiological status data collected simultaneously with the user images, the present invention determines the user's initial physical condition. Yoga movements that match the initial physical condition are selected to generate a training plan. Based on the demonstration movement data of the yoga movements in the training plan, the present invention uses real-world technology to simulate and display a virtual coach demonstrating the movements and explaining key points. This solves the problems of prior art and achieves the technical benefits of providing real-time movement guidance and personalized practice plans.

The following will be used in conjunction with drawings and embodiments to describe in detail the features and implementation methods of the present invention. The content is sufficient to enable anyone familiar with the relevant technology to easily and fully understand the technical means used by the present invention to solve the technical problems and implement them accordingly, thereby achieving the effects that can be achieved by the present invention.

The present invention can generate a suitable exercise plan based on the user's yoga posture and physiological state during the exercise, and provide movement guidance through real-world technology while the user practices yoga according to the exercise plan. The real-world technology mentioned in the present invention can be augmented reality or mixed reality, but the present invention is not limited to such.

The apparatus implementing the present invention may be a computing device. The computing device described herein includes, but is not limited to, one or more processing modules, one or more memory modules, and hardware components such as a bus that connects various hardware components (including the memory modules and processing modules). Through these hardware components, the computing device can load and execute an operating system, allowing the operating system to run on the computing device, and can also execute software or programs. The computing device also includes a housing, within which the aforementioned hardware components are housed.

The bus of the computing device provided in the present invention may include one or more types of buses, such as a data bus, an address bus, a control bus, an expansion bus, and/or a local bus. Buses used in computing devices include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Peripheral Component Interconnect (PCI) bus, the Video Electronics Standards Association (VESA) local bus, the Universal Serial Bus (USB) bus, and the PCI Express (PCI-E/PCIe) bus.

The processing module of the computing device proposed in the present invention is coupled to a bus. The processing module includes a register group or register space, which can be completely set on the processing chip of the processing module, or completely or partially set outside the processing chip and coupled to the processing chip via a dedicated electrical connection and/or via a bus. The processing module can be a central processing unit, a microprocessor, or any suitable processing element. If the computing device is a multi-processor device, that is, the computing device includes multiple processing modules, then the processing modules included in the computing device are the same or similar, and are coupled and communicated through a bus. In some embodiments, the processing module can interpret a computer instruction or a series of multiple computer instructions to perform specific calculations or operations, such as mathematical operations, logical operations, data comparison, copying/moving data, etc., to drive other hardware components in the computing device or run an operating system or execute various programs and/or modules. The computer instructions can be assembly language instructions, instruction set architecture instructions, machine instructions, machine-related instructions, microinstructions, firmware instructions, or initialization code or object code written in any combination of one or more programming languages, and the computer instructions can be executed entirely on a single computing device, partially on a single computing device, or partially on one computing device and partially on another connected computing device. The above-mentioned programming languages include object-oriented programming languages such as Common Lisp, Python, C++, Objective-C, Smalltalk, Delphi, Java, Swift, C#, Perl, Ruby, etc., and conventional procedural programming languages such as C or other similar programming languages.

Computing devices usually also include one or more chipsets. The processing module of the computing device can be coupled to the chipset or electrically connected to the chipset through a bus. The chipset is composed of one or more integrated circuits (ICs), including a memory controller and a peripheral input/output (I/O) controller, etc. In other words, the memory controller and the peripheral input/output (I/O) controller can be included in one IC, or can be implemented using two or more ICs. The chipset usually provides input/output and memory management functions, as well as multiple general-purpose and/or dedicated registers, timers, etc., wherein the above-mentioned general-purpose and/or dedicated registers and timers can be accessed or used by one or more processing modules coupled or electrically connected to the chipset. In some embodiments, the chipset may also be part of the processing module.

The processing module of a computing device can also access data from memory modules and mass storage areas installed on the computing device through a memory controller. The aforementioned memory modules include any type of volatile memory and/or non-volatile memory (NVRAM), such as static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), and flash memory. The aforementioned mass storage area can include any type of storage device or storage media, such as a hard drive, optical disc, flash drive, memory card, solid-state drive (SSD), or any other storage device. In other words, the memory controller can access data in static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, hard drives, and SSDs.

The processing module of a computing device can also connect to and communicate with peripheral devices or interfaces, such as peripheral output devices, peripheral input devices, communication interfaces, and various data or signal receiving devices, through a peripheral input/output controller via a peripheral input/output bus. A peripheral input device can be any type of input device, such as a keyboard, mouse, trackball, touchpad, or joystick. A peripheral output device can be any type of output device, such as a monitor or printer. The peripheral input device and peripheral output device can also be the same device, such as a touchscreen. The communication interface may include a wireless communication interface and/or a wired communication interface. The wireless communication interface may include an interface supporting wireless local area networks (such as Wi-Fi, Zigbee, etc.), Bluetooth, infrared, near-field communication (NFC), 3G/4G/5G mobile communication networks (cellular networks), or other wireless data transmission protocols. The wired communication interface may be an Ethernet device, a DSL modem, a cable modem, an asynchronous transfer mode (ATM) device, or an optical fiber communication interface and/or component. The data or signal receiving device may include a GPS receiver or a physiological signal receiver. The physiological signals received by the physiological signal receiver include, but are not limited to, heart rate and blood oxygen levels. The processing module can periodically poll various peripheral devices and interfaces, allowing the computing device to input and output data through various peripheral devices and interfaces, and also to communicate with another computing device having the hardware components described above.

The following will first illustrate the device implementing the present invention, using Figure 1, a schematic diagram of the components of the device for analyzing yoga movements and physiological conditions to generate a practice plan. As shown in Figure 1, the device 100 of the present invention includes a memory 110, a camera 120, a communication interface 130, a storage medium 140, an input device 150, a processor 170, and a bus 190. Processor 170 is connected to memory 110, camera 120, communication interface 130, storage medium 140, and input device 150 via bus 190.

Memory 110 can store one or more sets of computer instructions.

Camera 120 may include a circuit board, a lens assembly, and an image sensor (not shown). The lens assembly and the image sensor are connected via the circuit board. Camera 120 can capture images using the lens assembly and the image sensor. In the present invention, device 100 is not limited to including a single camera 120; it may also include multiple cameras 120 that capture images synchronously.

The communication interface 130 can be connected to an external network storage device or server and other network devices, and request and download data from the connected network device.

The storage medium 140 can store data or signals downloaded from the communication interface 130 , store data or signals provided to the processor 170 or required for the processor 170 to operate, and store data or signals generated by the processor 170 .

The input element 150 can provide input data through a peripheral input device of the device 100. For example, the input element 150 can input data through a keyboard, a mouse, a touchpad, or a touch screen.

The processor 170 may include modules such as a data acquisition module 210, an image analysis module 220, a data loading module 240, a state judgment module 250, a plan generation module 260, and a reality interaction module 290 as shown in the module schematic diagram of the present invention in "Figure 2", and may also include an additional action guidance module 270. In some embodiments, the processor 170 can execute computer instructions stored in the memory 110 and can generate the modules in "Figure 2" after executing the computer instructions; in other embodiments, the modules in "Figure 2" can be generated by one or more circuits and/or hardware components such as complete or partial chips, that is, the processor 170 includes hardware components that constitute the modules in "Figure 2", that is, the modules included in the processor 170 can be software modules or hardware modules, and the present invention has no special limitations.

The data acquisition module 210 is responsible for acquiring user images. The data acquisition module 210 can capture user images via the camera 120 or provide the input component 150 with the image storage path of the user image. The module then reads the user image from the storage medium 140 based on the image storage path, or connects to a data server (not shown) via the communication interface 130 to download the user image.

The data acquisition module 210 is also responsible for acquiring physiological status data collected synchronously with the acquired user images. The physiological status data acquired by the data acquisition module 210 includes but is not limited to heart rate, respiratory rate, blood oxygen concentration, and may even include strength, cardiopulmonary capacity, etc. Generally speaking, physiological status data can be generated by a wearable device (such as a smart bracelet, smart watch, heart rate sensor, etc.) worn by a user to detect the user's physiological status. The generated physiological status data can be directly transmitted to device 100 for storage or transmitted to real-world device 400 for transfer to device 100. In this way, data acquisition module 210 can receive physiological status data transmitted by the wearable device (not shown) or real-world device 400 via communication interface 130. It can also read pre-stored physiological status data from storage medium 140 based on the file storage path input by input component 150, or download physiological status data from a data server via communication interface 130. However, the methods for data acquisition module 210 to obtain physiological status data are not limited to the above.

The data acquisition module 210 can also acquire the user's practice images in real time. Generally speaking, the data acquisition module 210 can capture the user's practice images through the camera 120 or receive the practice images transmitted by the real device 400 through the communication interface 130.

The image analysis module 220 is responsible for analyzing the user image acquired by the data acquisition module 210 to generate initial posture data corresponding to the physiological status data. The initial posture data generated by the image analysis module 220 includes the positions and angles of multiple key parts of the user's body. The above-mentioned key parts include but are not limited to the top of the head, neck, shoulders, chest, elbows, hips, pelvic floor, knees, etc. The image analysis module 220 can analyze the user image through a deep learning model or computer vision technology. For example, if the user image contains depth data, the image analysis module 220 can use a pose estimation model such as OpenPose, PostNet, HRNet, etc. to obtain the features of the human body contour and local parts (such as head, limbs, etc.) in the user image through a convolutional neural network (CNN) to generate a heat map (Heatmap) representing the probability of each pixel in the user image being in the key part and an offset map (Offset) representing the vector value of each pixel in the user image deviating from the key part. Map), and based on the heat map and the offset map, determine the maximum value position of each key part in all the heat maps, and use the determined maximum value position as the key part of the user's body in the user image, and correspond each key part to the depth data in the user image, so as to determine the position coordinates and angle of each key part in space through the depth data of each key part; if the user image includes images captured synchronously at different angles, the image analysis module 220 can use the posture estimation model to detect the key parts of the user's body in the user image, and then calculate the position and angle of each key part in space through triangulation.

The image analysis module 220 can also analyze the training images obtained by the data acquisition module 210 to generate training posture data. Generally speaking, the image analysis module 220 can use the same process as described above to analyze the training images to generate training posture data.

Image analysis module 220 may also determine the yoga pose performed by the user in the user image based on the generated initial posture data, or may determine the yoga pose currently being practiced by the user based on the generated practice posture data. For example, image analysis module 220 may connect the key human body parts generated by the posture estimation model included in the initial posture data according to a predefined connection method for each key part to generate a human skeleton diagram. The generated human skeleton diagram may then be compared for similarity with standard skeleton diagrams of various yoga poses to determine the yoga pose corresponding to the initial posture data or the practice posture data, that is, to determine the yoga pose performed by the user, but the present invention is not limited to this.

Data loading module 240 can load standard posture data corresponding to the initial posture data generated by image analysis module 220. For example, standard yoga posture data identified by image analysis module 220 can be loaded from storage medium 140 or downloaded from a data server via communication interface 130. Data loading module 240 is also responsible for loading demonstration movement data corresponding to the current yoga movement. The standard posture data proposed in the present invention includes the positions, relative distances, and body angles of each joint under ideal conditions for the corresponding yoga movement.

The state determination module 250 is responsible for determining the user's initial physical state based on the initial posture data generated by the image analysis module 220 and the physiological state data acquired by the data acquisition module 210. The state determination module 250 can determine the user's initial physical state based on the synchronized initial posture data and physiological state data corresponding to the user's images acquired by the data acquisition module 210.

In more detail, the state judgment module 250 can judge the completion of the movement based on the initial posture data generated by the image analysis module 220 and the standard posture data loaded by the data loading module 240, and can also judge the smoothness of the movement based on the initial posture data, and can judge the initial body state based on the determined movement completion and movement smoothness. For example, the state judgment module 250 can connect the specific key parts in the initial posture data based on the connection relationship of each key part in the standard posture data to establish a skeleton, and can calculate the position and angle of each key part in the initial posture data, and can compare the position of each key part in the initial posture data with the standard posture data to find the deviation (such as the difference in angle or position). For example, the state judgment module 250 can judge the arm by whether the angle formed by the shoulder, elbow, and wrist in the initial posture data and the standard posture data has an angle deviation. For another example, the state judgment module 250 can calculate the distance and relative position between the initial posture data and the key parts in the standard posture data to determine the position deviation. For example, when the yoga pose is the downward dog pose, the state judgment module 250 can check the initial posture data and the standard posture data to determine whether the hip joint is sufficiently extended upward, and determine whether the movement is in place by comparing the position of the hip joint with the key parts of the hands and feet (elbows, wrists, knees, ankles, etc.). Afterwards, the deviation value of each key part can be weighted to obtain a deviation score. In some embodiments, if certain key areas are significantly correlated with movement completion (e.g., the role of the hip joint in Downward Dog), the deviation values of these key areas are given higher weights. For example, for a standard Downward Dog, the angle of the hip joint, the angle between the arm and the ground, and the position of the heels are key indicators. The state judgment module 250 assigns higher weights to the deviation values of these indicators, thereby focusing on calculating the deviation score based on the deviation values and weights of these indicators to evaluate the accuracy of the movement.

The state determination module 250 may also determine the degree of movement completion based on the practice posture data and the standard posture data analyzed by the image analysis module 220. For example, the state determination module 250 may calculate the degree of movement completion based on the similarity between a standard skeleton diagram formed by connecting key parts in the standard posture data and a human skeleton diagram formed by connecting key parts in the practice posture data, and the deviation between the relative distances and relative positions between key parts pre-defined for different yoga poses in the standard posture data and the relative distances and relative positions between key parts in the practice posture data, but the present invention is not limited to this.

The state judgment module 250 can also assess the user's fatigue status based on the changes in the completion degree of the movement, the smoothness of the movement, and the physiological status data. For example, the state judgment module 250 can track the completion degree (accuracy) of the user's yoga movements and determine whether the completion degree of the movement decreases as the practice time progresses, and then determine the accumulation of fatigue. For example, when the completion degree of the movement decreases, it means that the user's fatigue increases; similarly, the state judgment module 250 can also track the user's changes in the yoga movements. Fluency and judging the accumulated fatigue based on whether the fluency decreases as the practice time progresses. For example, when the fluency of the movement decreases, the user's fatigue increases. The state judgment module 250 can also judge the accumulated fatigue based on the changes in the physiological state data of the user when performing the same yoga movement. For example, when the heart rate or breathing rate increases, it indicates that the user's fatigue increases.

The plan generation module 260 is responsible for selecting a plurality of yoga poses that match the initial physical condition to generate a practice plan. The practice plan includes the selected yoga poses and the corresponding practice time for each selected yoga pose. The plan generation module 260 may select simpler variations for beginners and more challenging poses for advanced users. For example, the plan generation module 260 may download a difficulty list containing yoga poses and difficulty levels from the storage medium 140 or via the communication interface 130, and select yoga poses from the difficulty list that match the initial physical condition, but the present invention is not limited to this.

The plan generation module 260 can adjust the exercise plan based on the exercise completion rate determined by the state determination module 250 and the physiological status data obtained by the data acquisition module 210. For example, if the heart rate and respiratory rate increase abnormally, the plan generation module 260 can extend the rest period or reduce the difficulty of the exercise. At the same time, the plan generation module 260 can also gradually increase the difficulty of the exercise based on the user's progress. For example, if the user can consistently complete all the exercises in the existing plan and the user's exercise completion rate reaches or exceeds a certain value, the plan generation module 260 can add new, more difficult exercises to the exercise plan or extend the practice time of certain exercises in the exercise plan.

The action guidance module 270 can generate a guidance message when the deviation between the training posture data analyzed by the image analysis module 220 and the standard posture data reaches a threshold value.

The reality interaction module 290 is responsible for simulating a virtual coach using reality technology. The virtual coach can be a realistic person or a cute Q-version character designed using 3D modeling software. The present invention has no particular limitations.

The real-world interaction module 290 is also responsible for using the same real-world technology to simulate and display a virtual instructor performing the yoga poses and explaining the key points of the yoga poses based on the model movement data of the yoga poses currently being performed. The real-world interaction module 290 can also provide users with the ability to change perspectives, allowing them to observe the details of the yoga poses from different angles and learn the correct postures.

The reality interaction module 290 can also use the same reality technology to simulate a virtual coach to provide action guidance to the user based on the guidance information generated by the action guidance module 270.

Next, the system operation and method of the present invention will be explained using an embodiment. Please refer to FIG. 3A for a flow chart of the method for analyzing yoga movements and physiological conditions to generate a practice plan. In this embodiment, it is assumed that device 100 is a real-world server that provides connectivity to real-world devices, but the present invention is not limited to this.

The data acquisition module 210 of the device 100 can acquire a user image and physiological status data collected simultaneously with the acquired user image (step 310). The image analysis module 220 of the device 100 can analyze the user image acquired by the data acquisition module 210 to generate initial posture data corresponding to the physiological status data acquired by the data acquisition module 210 (step 320). In this embodiment, it is assumed that the initial posture data includes the positions and angles of multiple key parts of the user's body.

After the image analysis module 220 of the device 100 generates initial posture data, the state determination module 250 of the device 100 can determine the user's initial physical state based on the initial posture data generated by the image analysis module 220 and the physiological state data obtained by the data acquisition module 210 (step 330). In this embodiment, it is assumed that the state determination module 250 can compare the posture data with the standard posture data read by the data loading module 240 to determine the user's initial physical state.

After the state determination module 250 of the device 100 determines the initial physical state of the user, the plan generation module 260 of the device 100 can select a plurality of yoga poses that match the initial physical state and set the practice time of the selected yoga poses to generate a practice plan (step 340).

Afterwards, the reality interaction module 290 of the device 100 can simulate the virtual coach using reality technology (step 350). In this embodiment, it is assumed that the reality interaction module 290 can generate model data of the virtual coach and transmit the model data to the reality device 400 via the communication interface 130 of the device 100, so that the reality device 400 can use the received model data to augment the reality simulation and display the virtual coach.

At the same time, the reality interaction module 290 of the device 100 can also load the demonstration movement data corresponding to the current yoga pose (step 361), and can use reality technology to simulate and display a virtual instructor performing the current yoga pose and explaining the key points of the pose based on the demonstration movement data (step 365). In this embodiment, it is assumed that the reality interaction module 290 can transmit the input demonstration movement data to the reality device 400 via the communication interface 130 of the device 100, so that the reality device 400 can use augmented reality to simulate and display the virtual instructor performing the yoga pose based on the received demonstration movement data and explain the key points of the current yoga pose.

In this way, through the present invention, a practice plan suitable for the user can be generated, and when the user practices yoga according to the practice plan, movement guidance can be given through real-world technology.

In the above embodiment, after the reality interaction module 290 of the device 100 uses the reality technology to simulate and display the virtual coach performing the current yoga movement and explaining the key points of the movement according to the demonstration movement data (step 365), the data acquisition module 210 of the device 100 can also obtain the user's practice image and physiological state data in real time (step 371), the image analysis module 220 of the device 100 can analyze the user's practice image to generate practice posture data (step 375), and the state judgment module of the device 100 can also be used to analyze the user's practice image to generate practice posture data (step 375). 250 can determine the degree of movement completion based on the practice posture data and the standard posture data (step 381). The movement guidance module 270 of the device 100 can generate a guidance message when it is determined that the difference between the practice posture data and the standard posture data reaches a threshold value (step 385). The real-world interaction module 290 of the device 100 can use real-world technology to simulate a virtual coach to provide movement guidance based on the guidance message (step 391). At the same time, the plan generation module 260 of the device 100 can adjust the practice plan based on the movement completion and physiological status data (step 395).

In summary, the difference between the present invention and prior art lies in its ability to analyze user images to generate the user's initial posture data, determine the user's initial physical condition based on the user's initial posture data and physiological status data collected simultaneously with the user's images, select yoga movements that match the initial physical condition to generate a training plan, and then use real-world technology to simulate and display a virtual coach demonstrating the movements and explaining key points based on the demonstration movement data of the yoga movements in the training plan. This technical means can solve the problem of prior art that cannot provide users with confirmation whether their posture is correct, thereby achieving the technical effect of providing real-time movement guidance and personalized practice plans.

Furthermore, the method of analyzing yoga movements and physiological conditions to generate a practice plan of the present invention can be implemented in hardware, software, or a combination of hardware and software. It can also be implemented in a centralized manner in a computer system or in a distributed manner with different components distributed across several interconnected computer systems.

While the embodiments disclosed above are limited to the present invention, these descriptions are not intended to directly limit the scope of patent protection for this invention. Any modifications or alterations in the form and details of the present invention made by a person skilled in the art without departing from the spirit and scope of this invention are within the scope of patent protection for this invention. The scope of patent protection for this invention shall remain subject to the scope of the attached patent application.

100: Device 110: Memory 120: Camera 130: Communication Interface 140: Storage Media 150: Input Device 170: Processor 190: Bus 210: Data Acquisition Module 220: Image Analysis Module 240: Data Loading Module 250: State Detection Module 260: Project Generation Module 270: Action Guidance Module 290: Reality Interaction Module 400: Reality Device Step 310: Acquire user images and simultaneously collected physiological status data Step 320: Analyze user images to generate initial posture data corresponding to the physiological status data Step 330: Determine the user's initial physical state based on the initial posture data and physiological status data Step 340: Select yoga poses that match the initial physical state to generate a practice plan, which includes yoga poses and corresponding practice times Step 350: Use reality technology to simulate a virtual instructor Step 361: Load demonstration movement data corresponding to the current yoga pose Step 365: Using real-world technology, simulate and display a virtual instructor performing the current yoga pose based on the demonstration movement data and explain the key points of the movement. Step 371: Real-time acquisition of the user's practice images and physiological status data. Step 375: Analyze the user's practice images to generate practice posture data. Step 381: Determine the completion of the movement based on the practice posture data and the standard posture data. Step 385: Generate a coaching message when the difference between the practice posture data and the standard posture data reaches a threshold. Step 391: Based on the coaching information, use real-world technology to simulate a virtual coach providing movement guidance. Step 395: Adjust the exercise plan based on movement completion and physiological status data.

Figure 1 is a schematic diagram of the components of the device for analyzing yoga movements and physiological conditions to generate a practice plan according to the present invention. Figure 2 is a schematic diagram of the modules of the processor according to the present invention. Figure 3A is a flow chart of the method for analyzing yoga movements and physiological conditions to generate a practice plan according to the present invention. Figure 3B is a flow chart of the method for adjusting a practice plan and providing practice guidance according to the present invention.

Step 310: Obtain user images and simultaneously collected physiological status data

Step 320: Analyze user images to generate initial posture data corresponding to physiological state data

Step 330: Determine the user's initial physical condition based on the initial posture data and physiological status data.

Step 340: Select yoga poses that match your initial physical condition to generate a practice plan. The practice plan includes the yoga poses and the corresponding practice time.

Step 350: Use real-world technology to simulate a virtual coach

Step 361: Load the demonstration movement data corresponding to the current yoga movement.

Step 365: Use real-world technology to simulate and display a virtual instructor performing the current yoga movements based on the demonstration movement data and explaining the key points of the movements.

Claims (10)

A method for analyzing yoga movements and physiological conditions to generate a practice plan is applied to a device or a system. The method includes at least the following steps: obtaining a user image and physiological status data collected synchronously with the user image; analyzing the user image to generate initial posture data, the initial posture data including the positions and angles of multiple key body parts of the user, and the initial posture data corresponding to the physiological status data; determining an initial physical state of the user based on the initial posture data and the physiological status data; selecting multiple yoga movements that match the initial physical state to generate a practice plan, the practice plan including the yoga movements and the practice time corresponding to each yoga movement; A virtual coach is simulated using a real-world technology; demonstration movement data corresponding to a current yoga pose is loaded; based on the demonstration movement data, the virtual coach is simulated and displayed performing the current yoga pose and explaining the key points of the pose using the real-world technology; a practice image of the user performing the current yoga pose and corresponding physiological status data are obtained in real time; the practice image is analyzed to generate practice posture data; a deviation value of each key part is determined based on the relative distance and relative position between the key parts in the practice posture data and standard posture data, and a movement completion degree is calculated based on the deviation value of each key part; and the practice plan is adjusted based on the movement completion degree and the physiological status data. A method for analyzing yoga movements and physiological conditions to generate a practice plan as described in claim 1, wherein the method, after using the real-world technology to simulate and display the virtual coach performing the current yoga movement and explaining the key points of the movement based on the demonstration movement data, further includes the steps of acquiring and analyzing a practice image of the user in real time to generate practice posture data, generating a guidance message when the difference between the practice posture data and a standard posture data reaches a threshold value, and using the real-world technology to simulate the virtual coach providing movement guidance based on the guidance message. A method for analyzing yoga movements and physiological conditions to generate a practice plan as described in claim 1, wherein the method further includes, in the step of calculating the completion of the movement based on the deviation value of each key part, calculating the deviation score of each key part based on the deviation value and weight of each key part, and evaluating the completion of the movement based on the deviation score of each key part, wherein the weight of each key part is positively correlated with the completion of the current yoga movement. The method for analyzing yoga movements and physiological conditions to generate an exercise plan as described in claim 1, wherein the step of obtaining the physiological status data collected synchronously with the user's image is to detect the user's physiological status through a wearable device to generate the physiological status data. The method of analyzing yoga movements and physiological conditions to generate a practice plan as described in claim 1, wherein the step of determining the user's initial physical condition based on the initial posture data and the physiological condition data further includes determining a movement completion based on the initial posture data and a standard posture data, and determining the initial physical condition based on the movement completion and a movement smoothness of the initial posture data, and the step of assessing the user's fatigue condition based on changes in the movement completion, the movement smoothness, and the physiological condition data. A device for analyzing yoga movements and physiological conditions to generate a practice plan, the device comprising at least: a data acquisition module for acquiring a user image and physiological status data collected synchronously with the user image, and for instantly acquiring a current practice image of the user; an image analysis module for analyzing the user image to generate initial posture data, the initial posture data including positions and angles of multiple key parts of the user's body, and the initial posture data corresponding to the physiological status data, and for analyzing the practice image to generate practice posture data; a state judgment module for judging an initial body state of the user based on the initial posture data and the physiological status data, and for judging an initial body state of the user based on the practice posture data and standard posture data. The relative distance and relative position between each key part are used to determine the deviation value of each key part and calculate the completion degree of a movement according to the deviation value of each key part; a plan generation module is used to select a plurality of yoga movements that match the initial body state to generate a practice plan, the practice plan includes the yoga movements and the practice time corresponding to each yoga movement, and is used to calculate the completion degree of a movement according to the initial body state. The training plan is adjusted based on the movement completion degree and the physiological status data; a data loading module is used to load a demonstration movement data corresponding to the current yoga movement; and a reality interaction module is used to simulate a virtual coach using a real-world technology, and use the real-world technology to simulate and display the virtual coach performing the current yoga movement and explaining the key points of the movement based on the demonstration movement data. A device for analyzing yoga movements and physiological conditions to generate a practice plan as described in claim 6, wherein the device further includes a movement guidance module for generating a guidance message when the difference between the practice posture data generated by the image analysis module analyzing a practice image obtained in real time by the data acquisition module and a standard posture data reaches a threshold value, and the reality interaction module is further used to use the reality technology to simulate the virtual coach to provide movement guidance based on the guidance message. A device for analyzing yoga movements and physiological conditions to generate a practice plan as described in claim 6, wherein the state judgment module calculates the deviation score of each key part based on the deviation value and weight of each key part in the practice posture data and a standard posture data, and evaluates the completion of the movement based on the deviation score of each key part, wherein the weight of each key part is positively correlated with the completion of the current yoga movement. The device for analyzing yoga movements and physiological conditions to generate an exercise plan as described in claim 6, wherein the data acquisition module acquires the physiological state data generated by the wearable device detecting the physiological state of the user. A device for analyzing yoga movements and physiological conditions to generate an exercise plan as described in claim 6, wherein the state judgment module judges a movement completion degree based on the initial posture data and a standard posture data, judges the initial body state based on the movement completion degree and a movement smoothness of the initial posture data, and assesses the user's fatigue state based on changes in the movement completion degree, the movement smoothness, and the physiological state data.