CN113164055B - Mobile monitoring equipment, adjustment and processing method of physiological signals - Google Patents
Mobile monitoring equipment, adjustment and processing method of physiological signals Download PDFInfo
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Abstract
The application discloses a mobile monitoring device and a method for adjusting and processing physiological signals, wherein the mobile monitoring device comprises the following components: the device comprises a motion sensor, a physiological signal acquisition device, a memory and a processor; wherein, the motion sensor, the physiological signal acquisition device, the memory and the processor are connected by a lead wire; the motion sensor is used for collecting motion signals of the target object; the physiological signal acquisition device is used for acquiring physiological signals of a target object; a memory for storing an executable program; a processor for executing an executable program in memory that performs the following functions: acquiring physiological signals and motion signals of a target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object according to the motion signal characteristic information; determining a physiological signal characteristic based on the physiological signal; and adjusting the physiological signal characteristics according to the gesture indicated by the gesture information.
Description
Technical Field
The application relates to the field of medical equipment, in particular to mobile monitoring equipment and a physiological signal adjusting and processing method.
Background
With the development of medical technology and the improvement of people's cognition of medicine, the importance and attention of rapid postoperative rehabilitation are dramatically enhanced and improved. In the postoperative recovery period, medical staff hopes that the patient can get out of bed more and move, and the rapid recovery of the body is promoted. However, the traditional bedside monitoring limits the activity space of the patient, and long and complicated cables cannot enable the patient to comfortably move. Therefore, mobile monitoring becomes the first choice meeting the requirements, and plays a role in monitoring and measuring in the postoperative rapid recovery period.
For mobile monitoring, the patient can be interfered due to electrode pulling and other conditions in walking, getting on and off bed, clothes friction and other activities, and waveform signals can be seriously interfered, so that the accuracy of physiological signal measurement is affected.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
According to an aspect of an embodiment of the present application, there is provided a mobile monitoring device, including: the device comprises a motion sensor, a physiological signal acquisition device, a memory and a processor; wherein, the motion sensor, the physiological signal acquisition device, the memory and the processor are connected by a lead wire; the motion sensor is used for collecting motion signals of the target object; the physiological signal acquisition device is used for acquiring physiological signals of a target object; a memory for storing an executable program; a processor for executing an executable program in memory that performs the following functions: acquiring physiological signals and motion signals of a target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object according to the motion signal characteristic information; determining a physiological signal characteristic based on the physiological signal; and adjusting the physiological signal characteristics according to the gesture indicated by the gesture information.
According to an aspect of an embodiment of the present application, there is provided a method for adjusting a physiological characteristic, including: acquiring physiological signals and motion signals of a target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object according to the motion signal characteristic information; determining a physiological signal characteristic based on the physiological signal; and adjusting the physiological signal characteristics according to the gesture indicated by the gesture information.
According to another aspect of an embodiment of the present application, there is provided a method for processing a physiological signal, including: acquiring physiological signals and acceleration parameters of a target object; determining the motion signal characteristics of the target object based on the acceleration parameters, and determining the gesture information of the target object according to the motion signal characteristic information; determining physiological signal characteristics based on the physiological signals to obtain a physiological signal characteristic set; deleting invalid physiological signal characteristics from the physiological signal characteristic set by using the gesture information to obtain a target physiological signal characteristic set; and determining the physiological signal or the validity of alarm information corresponding to the physiological signal by using the characteristics in the target physiological signal characteristic set.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic diagram of a mobile monitoring device according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative motion signal analysis process according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of an alternative motion signal to assist in optimizing physiological signals according to an embodiment of the present application;
FIG. 4 is a flowchart of an alternative process for optimizing electrocardiographic features according to an embodiment of the present application;
FIG. 5 is a flow chart of a method of adjusting a physiological signal according to an embodiment of the present application;
Fig. 6 is a flow chart of a method of processing a physiological signal according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to better understand the embodiments of the present application, technical terms related to the embodiments of the present application are explained as follows:
QRS wave: left and right ventricular depolarization potentials and time, the first downward wave is the Q wave, the upward wave is the R wave, and the downward wave is the S wave.
When the patient is monitored in a moving way, the activities of the patient can interfere the parameters collected by the movable monitoring equipment, so that the accuracy of physiological signal measurement is influenced, and even the judgment of doctors on the condition of the patient is influenced, so that the rehabilitation of the patient is influenced. To solve the above problems, an embodiment of the present application provides a mobile monitoring device, which may include: the motion sensor can acquire motion signals of a target object and extract the motion signals from the motion signals; the processor can analyze the gesture information of the target object based on the motion signal, and then adjust the physiological signal based on the gesture information or determine whether to alarm based on the adjusted physiological signal. Wherein the motion sensor includes, but is not limited to: an acceleration sensor; physiological signals include, but are not limited to: electrocardiosignals, etc. The following is a detailed description.
Fig. 1 is a schematic structural diagram of a mobile monitoring device according to an embodiment of the present application. As shown in fig. 1, the mobile monitoring device includes:
A motion sensor 10, a physiological signal acquisition device 12, a memory 14, and a processor 16; wherein the motion sensor 10, the physiological signal acquisition device 12, the memory 14 and the processor 16 are connected by a lead wire; wherein: a motion sensor 10 for acquiring a motion signal of a target object; a physiological signal acquisition device 12 for acquiring a physiological signal of a target subject; a memory 14 for storing an executable program; a processor 16 for executing an executable program in the memory 14 that performs the following functions: acquiring physiological signals and motion signals of a target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object according to the motion signal characteristic information; determining a physiological signal characteristic based on the physiological signal; and adjusting the physiological signal characteristics according to the gesture indicated by the gesture information.
The motion sensor 10 may be worn as part of a wearable device on the body of a target object (i.e., a patient, for example), and the motion signals collected by the motion sensor may be transmitted to the processor 16, so as to analyze and identify the posture of the target object. In addition, the number of the motion sensors 10 may be plural, and in this case, the processor 16 may aggregate the signals collected by the plural motion sensors 10 to obtain plural motion signals; and comprehensively determining the attitude information based on the plurality of motion signals, for example, an average value of the values of the plurality of motion signals can be used as a basis for judging the attitude information, different weights can be distributed to the plurality of motion signals, and the weighted summation operation is performed on the plurality of motion signals, so that the attitude information is determined according to the weighted summation value.
In addition, the plurality of motion signals may be the same type of parameters acquired by the same type of sensor, for example, a plurality of acceleration values acquired by a plurality of acceleration sensors; it may also be different types of parameters collected by different types of sensors, for example, acceleration values and heart rate values collected by acceleration sensors and heart rate sensors.
In some embodiments of the present application, the motion signal and the physiological signal acquired by the motion sensor 10 and the physiological signal acquisition device 12, respectively, may be filtered to remove noise before being analyzed; after amplifying the noise-filtered signal, performing A/D conversion on the amplified signal, namely converting the analog signal into a data signal, thereby obtaining an analysis basis.
In addition, in order to further ensure the accuracy of the collected physiological signals, the differential or integral signals of the physiological signals can be calculated after the filtering and denoising treatment is carried out on the physiological information, and the final physiological signals are determined according to the differential or integral signals. The filtering denoising processing can be used for filtering power frequency interference, base drift and high-frequency noise interference of signals, and interference information can be filtered through integrating processing of the signals, so that signal peak value information is more outstanding.
Similarly, to further ensure the accuracy of the acquired motion signals, the motion signals can be subjected to band-pass filtering to remove base drift and high-frequency noise interference, so as to obtain relatively accurate motion signals.
For example, when determining the physiological signal characteristics, the physiological signal characteristics can be determined based on statistical information of some basic measurement information, for example, peak searching processing is performed on the physiological signal, basic measurement information such as amplitude, slope, width, frequency and the like of the peak is calculated, and then statistical information and clinical prior knowledge based on the basic measurement information are synthesized to calculate signal quality SQI and other time domain characteristics (such as characteristics of validity of the peak, type of the peak, peak-to-peak interval value, interval validity and the like) of the physiological signal; the signal can also be subjected to Fourier (fft) transformation to obtain characteristic information such as total energy TP, low-frequency energy LP, high-frequency energy HP and the like of the signal, and the ratio characteristic between different characteristic information can also be determined based on the Fourier transformation, namely, the ratio of two physical quantities is adopted as the physiological signal characteristic.
In some embodiments of the present application, the processor 16 is further configured to compare the gesture indicated by the gesture information with the specified gesture, and optimize the physiological signal feature when the gesture indicated by the gesture information is the specified gesture; when the gesture indicated by the gesture information is not the designated gesture, determining reliability information of the physiological signal, and when the reliability information indicates unreliability, optimizing the physiological signal characteristics according to the motion state information of the target object.
The physiological signal is optimized according to the first motion state information under the appointed gesture, so that the physiological signal characteristics are optimized, and the target physiological signal characteristics are obtained. Specifically, it may be represented that the weight of the physiological signal feature is adjusted according to the first motion state information, and the optimization of the physiological signal is achieved according to the weight. Among them, there are various optimization modes: for example, it may be expressed that the target physiological signal corresponding to the current motion state is determined according to the correspondence between the first motion state and the physiological signal; correcting the physiological signal according to the target physiological signal; for another example, when there are multiple physiological signal features, optimization of the physiological signal may be achieved by adjusting the weights of the different physiological signals, in particular: the processor is further configured to adjust the weights of the physiological signal characteristics according to: determining invalid physiological signal characteristics in the physiological signal characteristics according to the first motion state information; the weight of the invalid physiological signal feature is adjusted to zero, i.e. the invalid physiological signal feature is deleted.
Taking the above-mentioned designated gesture as a walking gesture as an example, when the designated gesture is a walking gesture, the above-mentioned first motion state information includes: the motion intensity of the target object and the step frequency of the target object; at this time, an invalid physiological signal feature among the physiological signal features may be as follows: when the step frequency is larger than a first threshold value and the motion intensity belongs to a first level, determining heartbeat interval information in the physiological signal characteristics as invalid physiological signal characteristics; determining that there is no homogeneity in the physiological signal characteristics and interval information of QRS waves which are not matched with the dominant QRS wave is an invalid physiological signal characteristic when the step frequency is greater than the second threshold and less than the first threshold and the motion intensity belongs to the second level; when the step frequency is smaller than the second threshold value and the motion intensity is of a third level, taking interval information of the QRS wave which is higher than a specified value, does not have uniformity and does not match the dominant QRS wave as invalid physiological signal characteristics; wherein, the motion intensity corresponding to the first level, the second level and the third level is reduced in turn.
It follows that the unsynchronized frequencies and the different motion intensities correspond to different optimization strategies for optimizing the physiological signal characteristics. As shown in fig. 3, after identifying the motion parameter features, classifying the motion parameters to obtain a first type of motion feature, a second type of motion feature and a third type of motion feature, wherein when the step frequency exceeds a first step frequency threshold value and the motion intensity exceeds a first intensity threshold value, the weights (which can be adjusted to 0) of the types of features are directly adjusted; for the second type of motion feature (step frequency less than the first step frequency threshold greater than the second step frequency threshold and motion intensity less than the first intensity threshold greater than the second intensity threshold), then the weights of the time domain or frequency domain features may be modified, and the SQI level or threshold may be altered; for a third type of motion feature (step frequency less than the second step frequency threshold and motion intensity less than the second intensity threshold), then the weights of the time domain or frequency domain features are modified and the SQI level or threshold is altered. For example, when the step frequency exceeds 90 and the motion intensity is high, the weight of the time domain/frequency domain characteristic is directly adjusted (can be set to 0), and the level or the threshold value of the physiological signal quality index (Signal Quality Index, simply called SQI) can be changed; when the step frequency is lower than 90 but exceeds 60, and the motion intensity is medium, the weight of the time domain feature or the frequency domain feature (set to 0-100 under different conditions) can be adjusted, and the level or the threshold value of the physiological signal quality index SQI is changed under certain conditions; when the step frequency is lower than 60 and the motion strength is weak, the weight of a part of the time domain features or the frequency domain features can be adjusted.
It should be noted that, in some embodiments of the present application, the step frequency may be divided without limitation of specific numbers, for example, a step frequency range corresponding to a specific form of walking may be determined based on the form; the motion intensity can be distinguished directly by a preset threshold without distinguishing strong, medium and weak; in addition, under the walking gesture, the walking gesture can be classified without the step frequency and the movement intensity, the types of fast walking, normal walking, slow walking and the like can be directly used for distinguishing, and at the moment, the corresponding weight and other information can be determined according to the walking type
When the gesture indicated by the gesture information does not belong to the specified gesture, the reliability information of the physiological signal needs to be considered at this time, and in some embodiments of the present application, the reliability information of the physiological signal may be determined by the processor 16: determining weights of physiological signal features; determining a target reliability index of the physiological signal according to the weight of the physiological signal characteristic and the reliability index corresponding to the physiological signal characteristic; comparing the target reliability index with a preset threshold; determining reliability information according to the comparison result, wherein the reliability information is determined to be reliable when the target reliability index is greater than a preset threshold value; and when the target reliability index is smaller than a preset threshold value, determining that the reliability information is unreliable.
For example: and counting the acquired SQI and time domain/frequency domain features, voting and scoring the weights of the features, acquiring the score of the reliability, setting a threshold value to classify the reliability level, and finally normalizing to be reliable/unreliable. Taking signal quality, QRS wave matching and QRS validity as examples, the weight of the signal quality is 50%, the weight of the QRS wave matching is 30%, and the weight of the QRS wave validity is 20%. The signal quality is good, a score of 10 is obtained (this score can be adjusted according to different thresholds, similar to the following), the QRS wave is well matched, a score of 10 is obtained, the QRS wave is effective, a score of 10 is obtained, and the total score is calculated to be 10. If the total score is greater than a threshold (e.g., 7), the physiological signal is determined to be reliable, otherwise it is unreliable.
Based on the analysis, when the posture of the target object is ambiguous, if the reliability of the physiological signal is higher, the signal characteristics and the alarm are not adjusted, and if the reliability of the electrocardio is lower, the parameters are optimized by utilizing the motion state; when the motion state is 0, no change is made, and when the motion state is greater than 0, different optimization strategies are provided for different parameters; by taking the electrocardio as an example, the validity judgment is carried out on the electrocardio heartbeat interval by combining the motion state, and the arrhythmia is shielded.
In some embodiments of the present application, when adjusting the weights of the above physiological signals, the target weights may be determined according to different motion signals, so as to achieve the adjustment of the weights, specifically: the first motion state information includes: at least one evaluation index for evaluating different motion signals in the first motion state; comparing each evaluation index with a corresponding threshold value for each evaluation index of the evaluation indexes of different parameters to obtain at least one comparison result; determining a target weight of the physiological signal feature according to the at least one comparison result; and adjusting the weight of the physiological signal feature to a target weight. It should be noted that the different motion signals refer to different values of the same type of parameter or different values of different types of parameter. For the latter, for example, different step frequencies and different motion intensities correspond to different weights, further for example: when the step frequency exceeds 90 and the motion strength is high, directly adjusting the weight of the time domain/frequency domain characteristic (which can be set to 0); when the step frequency is lower than 90 but exceeds 60, and the motion intensity is medium, the weight of the time domain/frequency domain characteristic can be adjusted (set to 0-100 under different conditions); when the step frequency is below 60 and the motion strength is weak, the weights of the partial time/frequency domain features can be adjusted.
In some embodiments of the present application, the preliminary optimization may be performed according to the motion state information of the target object before the optimization of the physiological signal according to the posture information, where the processor 16 is further configured to obtain the second motion state information of the target object before the posture information of the target object is obtained; optimizing the physiological signal characteristics according to the second motion state information to obtain initial physiological signal characteristics; and re-optimizing the initial physiological signal characteristics by using the first motion state information to obtain target physiological signal characteristics. That is, in this embodiment, the physiological signal of the target subject is optimized twice: 1, optimizing according to a motion state; and 2, optimizing according to the attitude information. By adopting the processing mode, the detection result of the physiological signal can be more accurate.
The second motion state information includes, but is not limited to: the motion intensity of the target object, the step frequency of the target object, and the like, but is not limited thereto. In addition, it should be noted that the first motion state information and the second motion state information may be the same or different, but the first motion state information is used to adjust the physiological signal feature together with the gesture information, and the second motion state information is used alone as a basis for adjusting the physiological signal feature.
In some embodiments of the application, to prevent false alarms, different alarm thresholds are set for different poses, for example: the processor is also used for determining an alarm threshold value corresponding to the gesture information after optimizing the physiological signal characteristics; comparing the index corresponding to the optimized physiological signal characteristics with an alarm threshold; when the gesture indicated by the gesture information is not the appointed gesture and the index corresponding to the physiological signal characteristic is greater than the alarm threshold value, alarming; and rejecting the alarm when the gesture indicated by the gesture information is a designated gesture and the index corresponding to the physiological signal characteristic is greater than the alarm threshold. For example, when the posture indicated by the posture information is lying or sitting, no alarm is given, and when the specified posture is walking and the condition (the index corresponding to the physiological signal characteristic is greater than the alarm threshold) is satisfied, an alarm is given.
Taking walking gesture as an example, the threshold value of each alarm can be correspondingly adjusted according to different step frequencies and different intensities, and the conditions are stricter when the signal characteristics are judged to be valid and the parameters are output to alarm. For example, as shown in fig. 4, when the step frequency exceeds 90 and the exercise intensity is high, the electrocardio-heartbeat interval is directly set to be invalid (jump for reducing heart rate and heart rate type alarm), QRS is classified to be normal (alarm for reducing ventricular arrhythmia), the level/threshold of SQI can be changed, and all the arrhythmia alarms of electrocardio can be directly set to be invalid; at step frequencies below 90 but above 60, and with exercise intensity in the middle, it may be set that there is no homogeneity and no interval of the electrocardiographic QRS wave matching the dominant QRS wave is invalid (jump to reduce heart rate and heart rate class alarm), and QRS wave is classified as normal (reduce ventricular arrhythmia alarm); when the step frequency is lower than 60 and the exercise intensity is low, only the interval of the electrocardio QRS wave which has higher electrocardio noise index, no uniformity and no match with the dominant QRS wave is invalid (jump of heart rate is reduced and heart rate type alarm is given), and the QRS wave is classified as normal (ventricular arrhythmia alarm is reduced); when the running is identified, the monitoring mode is directly changed into the running mode, and in the mode, the ST/QT switch is closed, the respiration monitoring is closed, the medium-level arrhythmia monitoring is closed and the like.
It should be noted that in the embodiment of the present application, alarm judgment needs to be performed according to the adjusted physiological signal, for example, after the invalid QRS wave interval is removed, the correct heart rate is calculated by using the valid interval; and after re-judging the QRS wave type, outputting arrhythmia alarm by utilizing the judged QRS wave type.
In some embodiments of the application, the walking posture may be determined by: acquiring a motion signal of a target object in a walking posture, wherein the motion signal comprises: peak statistics information of a motion signal of a target object in a preset time period and vector direction information of the motion signal; determining the number of identical peak information in the peak statistics; when the number is greater than a first threshold, determining that the target object is in a repetitive motion form; and determining that the target object is in the walking posture based on the vector direction information when the target object is determined to be in the repetitive motion form. For example, acquiring search wave information and amplitude information, and counting the mean value, variance and other information of time domain characteristic information; judging that the repeated motion forms exist based on the counted peak searching number, and judging the walking gesture based on the direction information of the motion sensor; and counting the number of the searched peaks in a period of time, and calculating the frequency of the road according to the number of the searched peaks.
In some embodiments of the present application, the gesture information may further include: a stationary posture; the stationary attitude is determined by: the method comprises the steps of obtaining a direction vector and a motion strength when a target object is in a static attitude; matching the direction vector with a preset direction vector to obtain a matching result; and when the matching result indicates that the direction vector is consistent with the preset direction vector and the motion intensity is smaller than a second threshold value, determining that the target object is in a static posture. Wherein the static posture includes, but is not limited to: the target object is in a lying state or a sitting state.
As shown in fig. 2, the process of analyzing the motion signal includes the following processing steps:
Step S202, collecting a motion signal by a motion sensor (e.g., an acceleration sensor), and extracting a temporal feature based on the motion signal, where the temporal feature includes: searching wave information and amplitude information, and counting the mean value, variance and other information of time domain characteristic information; after the motion signal is collected, two independent processes are executed: steps S204 to S208 and steps S210 to S214.
Step S204, the signal is subjected to peak searching processing, peak information is counted, and the step S206 is performed.
Step S206, judging that the repetitive motion form exists based on the counted wave crest number, and judging that the current object is in a walking posture based on the direction information of the motion sensor;
Step S208, step frequency and walking intensity are calculated, and the judgment threshold value of the exercise intensity is updated. Counting the number of search peaks in a period of time, and calculating walking frequency; based on the statistical SVM (support vector machine) value, identifying the motion strength by utilizing an adaptive threshold;
in step S210, an acceleration value is calculated and a direction vector is determined. Based on the accelerometer with fixed direction, the direction vector of lying/sitting can be calculated;
In step S212, the direction vector of the motion signal is matched with the lying and sitting direction vector. And matching the direction vector of the accelerometer obtained through calculation with the direction vector in the lying posture or the sitting posture.
Step S214, determining the gesture of the target object according to the matching result. And judging whether the target object is in a lying state or a sitting state according to the matching result and the identified movement intensity, wherein when the matching result and the movement intensity are lower than a certain threshold value, the target object is determined to be in the lying state or the sitting state.
In addition, the above optimization is performed on the physiological signals collected by the target object during the moving process (e.g. walking), and in some embodiments of the present application, when the target object is determined to be in the lying sitting posture, the motion intensity and the reliability of the physiological signals in the lying sitting posture may also be combined to perform the optimization processing on the physiological signals, and the specific optimization process may be referred to the above related description and will not be repeated herein.
In some embodiments of the present application, the above-described pose information is obtained by: acquiring an acceleration signal of a target object through an accelerometer arranged on the wearable equipment; determining a motion signal characteristic of the target object based on the acceleration signal, and determining third motion state information based on the motion signal characteristic; and determining the gesture information through the third motion state information.
It should be noted that, in the embodiment of the present application, the information included in the first motion state information, the second motion state information, and the third motion state information may be all the same or may be partially the same.
In some embodiments of the application, to prevent false measurements, some conditions may be set that trigger the measurement, such as: the processor is also used for determining the gesture information of the target object based on the acceleration signal of the target object when the timing time is reached; when the gesture information indicates that the target object is in the first gesture and the motion intensity exceeds a first threshold value, suspending measurement of the physiological signal of the target object and restarting timing; when the first preset time length after restarting timing is reached and the gesture information is the second gesture, the physiological signal of the target object is started to be measured, and the movement intensity of the first gesture is higher than that of the second gesture.
The processor is also used for stopping measurement when the diastolic pressure of the target object is detected in a preset detection period after the physiological signal of the target object is measured; in a preset detection period, when the diastolic pressure is not detected, the acceleration signal is collected again, and the gesture of the target object is determined again based on the collected acceleration signal; and stopping measurement when the re-determined gesture is the first gesture and the time for keeping the first gesture reaches the second preset duration.
Taking the physiological signal as a mechanical physiological signal as an example, a non-invasive blood pressure (NIBP) signal is selected from the mechanical physiological signal as the physiological signal. The motion sensor is an accelerometer, and the acquired motion signal is an acceleration signal.
The process of using motion states to assist NIBP measurement is as follows:
1. when the timing clock meets the measurement conditions, outputting the human body gesture by using an acceleration signal analysis function, and delaying measurement and re-timing when the human body gesture is recognized as fast walking;
2. When the user is identified as walking slowly, lying/sitting, the measurement is started;
3. In the wave searching period of the pressure platform, if the diastolic pressure is directly searched, the measurement is finished; if the diastolic pressure is not searched, the motion gesture is identified again and the motion time is counted, and when the motion time exceeds a threshold value, the measurement is abandoned and the measurement is finished;
4. if there is no movement or the movement duration does not reach the threshold value, marking the plateau time with the pulse signal, executing step 3
In some embodiments of the present application, the motion sensor 10 and the processor 16 are integrated in a single device; or the motion sensor 10 and the physiological signal acquisition device 12 are integrated in a single device.
Based on the mobile monitoring equipment provided by the embodiment of the application, the human body posture can be judged by utilizing the motion acquired by the motion sensor, and the physiological signal analysis process is optimized according to the human body posture, so that the accuracy of physiological signal measurement is improved, and the wrong parameter output and false alarm are reduced.
In the embodiment of the application, the gesture recognition can be performed by adopting the acceleration information acquired by the acceleration sensor at the neckline: different postures such as walking, lying, sitting and the like can be identified; wherein different step frequencies and different intensities can be identified for the walking gesture, thus involving the way in which the gesture is identified by means of one accelerometer. Thus, based on gesture recognition, a comprehensive decision approach is given: a. when the walking gesture is identified, the threshold value of each alarm can be correspondingly adjusted according to different step frequencies and different intensities, and when the signal characteristics are judged to be valid and the parameters are output to alarm, the conditions are stricter. b. The identification is lying or sitting, although the signal characteristics and the alarm are not easily corrected when the movement state exists; c. when the posture is ambiguous, the motion state is applied to optimize the physiological parameters.
The following describes in detail the workflow of the mobile monitoring device in connection with fig. 5, the principle of the workflow is as follows: acquiring physiological signals and motion signals of a target object; analyzing the motion signal to obtain the motion signal characteristics of the target object; determining the gesture information of the target object according to the motion signal characteristic information; determining a physiological signal characteristic based on the physiological signal; and adjusting the physiological signal characteristics according to the gesture indicated by the gesture information. As shown in fig. 5, the process includes:
step S502, respectively acquiring a physiological signal and a motion signal of a target object through a physiological signal acquisition device and a motion sensor;
step S504, the processor analyzes the motion signal to obtain the motion signal characteristics of the target object;
step S506, the processor determines the attitude information of the target object according to the motion signal characteristic information;
Step S508, the processor determines physiological signal characteristics based on the physiological signal;
Step S510, judging whether the gesture indicated by the gesture information is a designated gesture; if yes, go to step S512, otherwise go to step S514;
Step S512, optimizing the physiological signal characteristics;
Step S514, determining reliability information of the physiological signal, and optimizing the physiological signal feature according to the motion state information of the target object when the reliability information indicates unreliable. Wherein, can optimize according to the motion level that the motion state information indicates, the motion level includes but is not limited to: exercise intensity level, movement speed level, etc.
Step S516, calculating physiological parameters according to the optimized physiological signal characteristics;
In step S518, the validity of an abnormal physiological parameter alarm is determined, wherein the abnormal physiological parameter alarm is an alarm generated when an abnormality of a physiological parameter is detected.
In some embodiments of the application, the reliability information of the physiological signal may be determined by: determining weights of physiological signal features; determining a target reliability index of the physiological signal according to the weight of the physiological signal characteristic and the reliability index corresponding to the physiological signal characteristic; comparing the target reliability index with a preset threshold; determining reliability information according to the comparison result, wherein the reliability information is determined to be reliable when the target reliability index is greater than a preset threshold value; and when the target reliability index is smaller than a preset threshold value, determining that the reliability information is unreliable.
When the physiological signal characteristics are optimized, the physiological signal characteristics can be optimized based on the first motion state information under the specified gesture so as to obtain target physiological signal characteristics. Specifically: and adjusting the weight of the physiological signal characteristic according to the first motion state information. Wherein, confirm the invalid physiological signal characteristic in the physiological signal characteristic according to the first movement state information; the weight of the invalid physiological signal feature is adjusted to zero, i.e. the invalid physiological signal feature is deleted.
In some optional embodiments of the application, the above specified gesture comprises: walking posture; the first motion state information includes: the motion intensity of the target object and the step frequency of the target object; when the step frequency is larger than a first threshold value and the motion intensity belongs to a first level, determining heartbeat interval information in the physiological signal characteristics as invalid physiological signal characteristics; determining that there is no homogeneity in the physiological signal characteristics and interval information of QRS waves which are not matched with the dominant QRS wave is an invalid physiological signal characteristic when the step frequency is greater than the second threshold and less than the first threshold and the motion intensity belongs to the second level; when the step frequency is smaller than the second threshold value and the motion intensity is of a third level, taking interval information of the QRS wave which is higher than a specified value, does not have uniformity and does not match the dominant QRS wave as invalid physiological signal characteristics; wherein, the motion intensity corresponding to the first level, the second level and the third level is reduced in turn.
In some alternative embodiments of the application, the weights of the physiological signal characteristics may be adjusted in dependence of different motion states, i.e. the first motion state information comprises: at least one evaluation index for evaluating different motion signals in the first motion state; comparing each evaluation index with a corresponding threshold value for each evaluation index of the evaluation indexes of different parameters to obtain at least one comparison result; determining a target weight of the physiological signal feature according to the at least one comparison result; and adjusting the weight of the physiological signal feature to a target weight.
In addition, to ensure the optimization effect, the physiological signal characteristics can be optimized twice: acquiring second motion state information of the target object before acquiring the posture information of the target object; optimizing the physiological signal characteristics according to the second motion state information to obtain initial physiological signal characteristics; and re-optimizing the initial physiological signal characteristics by using the first motion state information under the specified gesture to obtain target physiological signal characteristics.
For optimized physiological signal characteristics, besides being used for calculating final physiological parameters, the method can also be used for determining the validity of alarms, for example: determining an alarm threshold corresponding to the gesture information; comparing the index corresponding to the optimized physiological signal characteristics with an alarm threshold; when the gesture indicated by the gesture information is not the appointed gesture and the index corresponding to the physiological signal characteristic is greater than the alarm threshold value, alarming; and rejecting the alarm when the gesture indicated by the gesture information is a designated gesture and the index corresponding to the physiological signal characteristic is greater than the alarm threshold.
Taking the above specified posture as a walking posture as an example, the walking posture is determined by: acquiring a motion signal of a target object in a walking posture, wherein the motion signal comprises: peak statistics information of a motion signal of a target object in a preset time period and vector direction information of the motion signal; determining the number of identical peak information in the peak statistics; when the number is greater than a first threshold, determining that the target object is in a repetitive motion form; and determining that the target object is in the walking posture based on the vector direction information when the target object is determined to be in the repetitive motion form.
For another example, when the above specified posture is a stationary posture, the stationary posture is determined by: the method comprises the steps of obtaining a direction vector and a motion strength when a target object is in a static attitude; matching the direction vector with a preset direction vector to obtain a matching result; and when the matching result indicates that the direction vector is consistent with the preset direction vector and the motion intensity is smaller than a second threshold value, determining that the target object is in a static posture.
In some embodiments of the present application, the acceleration parameter of the target object is obtained by an accelerometer provided on the wearable device; a motion signal characteristic of the target object is determined based on the acceleration parameter.
Fig. 6 is a flow chart of a method of processing a physiological signal according to an embodiment of the present application. As shown in fig. 6, the method includes:
step S602, obtaining physiological signals and acceleration parameters of a target object;
Step S604, determining the motion signal characteristics of the target object based on the acceleration parameters, and determining the gesture information of the target object according to the motion signal characteristic information;
step S606, determining physiological signal characteristics based on the physiological signals to obtain a physiological signal characteristic set;
step S608, deleting invalid physiological signal characteristics from the physiological signal characteristic set by utilizing the gesture information to obtain a target physiological signal characteristic set;
step S610, determining the physiological signal or the validity of the alarm information corresponding to the physiological signal by using the features in the target physiological signal feature set.
It should be noted that, the preferred implementation of the embodiment shown in fig. 6 may refer to the related descriptions of the embodiments shown in fig. 1 to 5, which are not repeated here.
Based on the above scheme provided by the embodiment of the application, the following effects can be achieved:
1. the source of the disturbance is identified. Based on human body gesture recognition, an interference source is recognized, false alarms are reduced, and accuracy of parameters is improved.
2. The correlation of physiological parameter interference and motion signals is improved. For example, it is recognized that the human body is moving, and the recognized posture is walking, if the interference of the physiological parameter occurs and the false alarm occurs at this time, it can be basically confirmed that the interference is caused by walking and the false alarm is shielded, and the reliability of the electrocardiographic signal is not purely relied on as an admission condition.
3. And error correction in uncorrelation is reduced. When the user is identified to lie or sit still, even if the acceleration movement is changed greatly due to trembling of hands, the alarm cannot be easily corrected, and missing report is avoided.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
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| CN113892954A (en) * | 2021-09-30 | 2022-01-07 | 联想(北京)有限公司 | Wearable electrocardiogram monitoring equipment and information determination method |
| CN114668388B (en) * | 2022-02-16 | 2025-06-20 | 深圳技术大学 | Intelligent elderly health monitoring method, device, terminal and storage medium |
| CN119164452B (en) * | 2024-11-13 | 2025-03-04 | 江西众加利高科技股份有限公司 | Bridge safety early warning method, device, electronic equipment and storage medium |
| CN120748784B (en) * | 2025-08-27 | 2025-11-07 | 深圳聚瑞云控科技有限公司 | A method, apparatus, device, and storage medium for prompting cadence adjustment. |
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| US7733224B2 (en) * | 2006-06-30 | 2010-06-08 | Bao Tran | Mesh network personal emergency response appliance |
| US10596381B2 (en) * | 2010-12-20 | 2020-03-24 | Cardiac Pacemakers, Inc. | Physiologic response to posture |
| WO2012129413A1 (en) * | 2011-03-24 | 2012-09-27 | Draeger Medical Systems, Inc. | Apparatus and method for measuring physiological signal quality |
| CN102988036B (en) * | 2012-12-26 | 2014-08-06 | 中国科学院自动化研究所 | Method for measuring pulse rate |
| CN203183567U (en) * | 2013-03-29 | 2013-09-11 | 刘伟 | Arm strength training device for physical education |
| CN104434312B (en) * | 2013-09-13 | 2017-10-24 | 深圳迈瑞生物医疗电子股份有限公司 | Custodial care facility and its physiological parameter processing method and system |
| DE112015007313B4 (en) * | 2014-09-02 | 2025-02-13 | Apple Inc. | physical activity and training monitor |
| CN106293032B (en) * | 2015-06-08 | 2021-09-24 | 北京三星通信技术研究有限公司 | Portable terminal equipment and its control method and device |
| CN105852826B (en) * | 2016-03-22 | 2018-10-09 | 北京奇虎科技有限公司 | The method that terminal and terminal determine physiologic information |
| CN116999054A (en) * | 2017-05-19 | 2023-11-07 | 北京麦迪克斯科技有限公司 | Physiological information acquisition device and method based on motion state |
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