Physiological signal measuring method and related equipment

Abstract

The embodiment of the invention provides a physiological signal measurement method and related equipment, and the method comprises the steps: determining a physiological signal initial value and probability density distribution data corresponding to each photoelectric volume pulse wave method data fragment based on each photoelectric volume pulse wave method data fragment; each photoplethysmography data fragment is obtained by dividing photoplethysmography data, and the probability density distribution data is used for representing probabilities corresponding to different physiological signal initial values; determining an effective physiological signal initial value from the plurality of physiological signal initial values according to a matching relationship between the physiological signal initial value corresponding to each photoplethysmography data fragment and the probability density distribution data; and determining a physiological signal measurement result based on the effective physiological signal initial value. According to the physiological signal measurement method provided by the embodiment of the invention, the physiological signal value is determined based on the physiological signal initial value with relatively high confidence, so that the accuracy of the physiological signal value can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of electronic technology, and in particular to a physiological signal measurement method and related equipment. Background Art

[0002] Photo Plethysmo Graphy (PPG) is a non-invasive physiological signal measurement technology that uses light-emitting diodes (LEDs) to illuminate human skin and uses photodiodes to measure changes in the intensity of reflected light caused by blood flow, thereby obtaining a periodic waveform containing information on the pulsating changes in blood volume during the heart beat cycle.

[0003] The method of calculating systolic and diastolic pressures based on the periodic waveform obtained through PPG to obtain physiological signal measurement results has the problem of low accuracy of physiological signal measurement results due to the influence of individual differences.

[0004] Therefore, a solution is urgently needed to solve the above technical problems. Summary of the invention

[0005] The present application provides a physiological signal measurement method and related equipment, the purpose of which is to improve the accuracy of physiological signal measurement results.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In the first aspect: an embodiment of the present application provides a physiological signal measurement method, by determining a physiological signal initial value and probability density distribution data corresponding to each PPG data segment based on each PPG data segment; each PPG data segment is obtained by dividing the PPG data, and the probability density distribution data is used to represent the probabilities corresponding to different physiological signal initial values; according to the matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, a valid physiological signal initial value is determined from multiple physiological signal initial values; based on the valid physiological signal initial value, a physiological signal measurement result is determined.

[0007] In the embodiment of the present application, according to the matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, the physiological signal initial value with low confidence can be filtered out, and the valid physiological signal initial value can be determined from multiple physiological signal initial values. The physiological signal measurement method provided in the embodiment of the present application determines the physiological signal value based on the physiological signal initial value with high confidence, which can effectively improve the accuracy of the physiological signal value.

[0008] In a possible implementation, according to the matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, the valid physiological signal initial value is determined from multiple physiological signal initial values, including: according to the matching relationship between each physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, the probability interval corresponding to each physiological signal initial value is determined; when the probability interval corresponding to a physiological signal initial value is the first interval or the second interval, the physiological signal initial value is determined as a valid physiological signal initial value, and when the probability interval corresponding to a physiological signal initial value is the third interval, the physiological signal initial value is determined as an invalid physiological signal initial value; the integral value of the probability density distribution data in the first interval is the highest, and the integral value in the second interval is less than the integral value in the first interval, and greater than the integral value in the third interval. In the embodiment of the present application, the effective physiological signal initial value is determined by dividing the probability interval, which reduces the difficulty of the classification task.

[0009] In a possible implementation, the effective physiological signal initial values ​​all correspond to the second interval, and the physiological signal measurement results are determined based on the effective physiological signal initial values, including: determining the probability of each effective physiological signal initial value based on probability density distribution data; multiplying each effective physiological signal initial value by the probability of each effective physiological signal initial value to determine the weighted physiological signal initial value corresponding to each effective physiological signal initial value; and determining the physiological signal measurement results based on the average of each weighted physiological signal initial value, which can effectively improve the accuracy of the physiological signal measurement results.

[0010] In a possible implementation, the effective physiological signal initial values ​​include effective physiological signal initial values ​​corresponding to the first interval and effective physiological signal initial values ​​corresponding to the second interval. Based on the effective physiological signal initial values, the physiological signal measurement results are determined, including: for the effective physiological signal initial values ​​corresponding to the second interval, the product of each effective physiological signal initial value and the probability of the effective physiological signal initial value is determined as a weighted physiological signal initial value corresponding to each effective physiological signal initial value; the probability of the effective physiological signal initial value is determined based on probability density distribution data; the physiological signal measurement result is determined based on the weighted physiological signal initial value of the effective physiological signal initial value corresponding to the second interval and the mean of the effective physiological signal initial value corresponding to the first interval, which can effectively improve the accuracy of the physiological signal measurement result.

[0011] In a possible implementation, a valid physiological signal initial value is determined from multiple physiological signal initial values ​​according to a matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, including: determining the probability of the physiological signal initial value according to a matching relationship between each physiological signal initial value corresponding to each PPG data segment and the probability density distribution data; when the probability of a physiological signal initial value is greater than a first threshold, determining the physiological signal initial value as a valid physiological signal initial value; when the probability of a physiological signal initial value is less than or equal to the first threshold, determining the physiological signal initial value as an invalid physiological signal initial value.

[0012] In a possible implementation, based on each photoplethysmography PPG data segment, determining the physiological signal initial value and probability density distribution data corresponding to each PPG data segment includes: based on a regression model, performing physiological signal numerical regression on each PPG data segment to determine the physiological signal initial value corresponding to each PPG data segment; based on a classification model, performing physiological signal classification on each PPG data segment to determine the probability density distribution data corresponding to each PPG data segment; the regression model and the classification model are obtained by training based on training set data; the training set data generation process includes: obtaining PPG data and standard data collected in multiple time periods; dividing the PPG data collected in each time period into a first data segment and a second data segment; mapping the first data segment of the Nth time period with the standard data of the N-1th time period, and mapping the second data segment of the Nth time period with the standard data of the Nth time period to obtain the training set data, where N is a positive integer greater than 0. In the embodiment of the present application, determining the physiological signal initial value and probability density distribution data based on the regression model and the classification model can effectively improve the efficiency and accuracy of determining the physiological signal measurement results.

[0013] In one possible implementation, the probability density distribution data is Gaussian distributed, and the training step of the classification model includes: taking the training set data as input, outputting the probability density distribution data corresponding to the training set data, and the mean of the probability density distribution data is determined based on the standard data in the training set data. In the embodiment of the present application, the probability density distribution function based on Gaussian distribution realizes the classification of physiological signals, which reduces the difficulty of the classification task and improves the accuracy of the classification.

[0014] In a possible implementation, when the physiological signal is blood pressure, the physiological signal initial value includes a systolic pressure initial value and a diastolic pressure initial value, the probability density distribution data includes first distribution data corresponding to the systolic pressure initial value and second distribution data corresponding to the diastolic pressure initial value, and according to the matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, determining the effective physiological signal initial value from multiple physiological signal initial values, including: determining the effective systolic pressure initial value from multiple systolic pressure initial values ​​according to the systolic pressure initial value corresponding to each PPG data segment and the first distribution data, determining the effective diastolic pressure initial value from multiple diastolic pressure initial values ​​according to the diastolic pressure initial value corresponding to each PPG data segment and the second distribution data; determining the physiological signal measurement result based on the effective physiological signal initial value, including: determining the mean of the effective systolic pressure initial values ​​as the systolic pressure value, and determining the effective diastolic pressure initial value as the diastolic pressure value; determining the physiological signal measurement result based on the systolic pressure value and the diastolic pressure value.

[0015] In a possible implementation, before determining the physiological signal initial value and probability density distribution data corresponding to each PPG data segment based on each photoplethysmography PPG data segment, the method also includes: receiving PPG data sent by a wearable device; after determining the physiological signal measurement result based on the effective physiological signal initial value, the method also includes: sending the physiological signal value to the wearable device to display the physiological signal measurement result on the display interface of the wearable device for user viewing.

[0016] In a possible implementation, based on the effective physiological signal initial value, determining the physiological signal measurement result includes: when there are multiple effective physiological signal initial values, determining the average of the multiple effective physiological signal initial values ​​as the physiological signal value; based on the physiological signal value, determining the level corresponding to the physiological signal value; and determining the physiological signal value and / or level as the physiological signal measurement result. In the embodiment of the present application, the physiological signal measurement result can be the physiological signal value and / or level, which is convenient for users to view and improve the user experience.

[0017] In the second aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the steps of the physiological signal measurement method as described above according to the instructions in the program code.

[0018] A third aspect: The present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a physiological signal measurement method as described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of physiological signal measurement provided in an embodiment of the present application; Figure 2 A schematic diagram of a periodic waveform provided in an embodiment of the present application; Figure 3 A schematic diagram of an application scenario of a physiological signal measurement method provided in an embodiment of the present application; Figure 4 A flow chart of a physiological signal measurement method provided in an embodiment of the present application; Figure 5 A data partitioning schematic diagram provided for an embodiment of the present application; Figure 6 Another data partitioning schematic diagram provided for an embodiment of the present application; Figure 7 A data collection schematic diagram provided for an embodiment of the present application; Figure 8 Another data collection schematic diagram provided for an embodiment of the present application; Fig. 9 A schematic diagram of data classification based on hard labels provided in an embodiment of the present application; Fig.10 A schematic diagram of data classification based on soft tags provided in an embodiment of the present application; Fig.11 A schematic diagram of data classification based on Gaussian labels provided in an embodiment of the present application; Fig.12 A schematic diagram of a blood pressure measurement method provided in an embodiment of the present application; Fig.13 A schematic diagram of a physiological signal measurement algorithm provided in an embodiment of the present application; Fig.14 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application; Fig.15 A software structure block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The terms "first", "second", "third", etc. in the specification, claims and drawings of this application are used to distinguish different objects rather than to limit a specific order.

[0021] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0022] Photo Plethysmo Graphy (PPG) is a non-invasive technique for measuring physiological signals. Figure 1 As shown in the figure, when the user's finger touches the PPG sensor, the light emitted by the light emitting diode (LED) in the PPG sensor can pass through the tissues and arteries and veins in the skin, and be absorbed and reflected back to the photodetector (PD) of the photodiode. When the user's measurement site does not move significantly, the absorption of light by the muscles, bones, veins and other connecting tissues in the measurement site remains basically unchanged, but the blood is different. Since the blood in the artery is constantly flowing, the absorption of light by the blood will change.

[0023] When the optical signal is converted into an electrical signal, the electrical signal can be divided into a DC signal and an AC signal because the absorption of light by the artery changes while the absorption of light by other tissues remains basically unchanged. Among them, the AC signal can characterize the characteristics of blood flow. By extracting the AC signal from the electrical signal, a periodic waveform containing the pulsating change information of blood volume can be obtained based on the AC signal, such as Figure 2 As shown in the figure, a periodic waveform containing pulsating change information of blood volume obtained based on PPG is shown. The trough of the periodic waveform indicates that the heart is in a diastolic state, and the peak indicates that the heart is in a systolic state.

[0024] First, although systolic and diastolic blood pressure can be calculated based on the periodic waveform obtained through PPG to obtain physiological signal measurement results, this method is greatly affected by individual differences and has the problem of low accuracy of physiological signal measurement results.

[0025] Secondly, in order to obtain more accurate physiological signal measurement results, after the meta-model is trained based on the data set, the meta-model can be personalized calibrated based on the user's physiological signal data to obtain a physiological signal prediction model for measuring the user's physiological signals, which has the problem of poor universality.

[0026] Based on this, an embodiment of the present application provides a physiological signal measurement method, which divides the PPG data into multiple PPG data segments, and determines the physiological signal initial value and probability density distribution data corresponding to each PPG data segment based on each PPG data segment; wherein the probability density distribution data is used to represent the probabilities corresponding to different physiological signal initial values. On this basis, in an embodiment of the present application, according to the matching relationship between the physiological signal initial value and the probability density distribution data corresponding to each PPG data segment, the physiological signal initial value with low confidence can be filtered out, and the valid physiological signal initial value can be determined from the multiple physiological signal initial values, and the physiological signal measurement result can be determined based on the valid physiological signal initial value. The physiological signal measurement method provided in the embodiment of the present application determines the physiological signal value based on the physiological signal initial value with high confidence, which can effectively improve the accuracy of the physiological signal value.

[0027] The following introduces an application scenario of a physiological signal measurement method provided by an embodiment of the present application in conjunction with the accompanying drawings.

[0028] For example, the electronic device is an electronic watch and the physiological signal is blood pressure. Figure 3 As shown, after the user wears the electronic watch, by clicking the blood pressure option 1301 in the first interface 1300 of the electronic watch, the electronic watch can respond to the user's operation, obtain photoplethysmography (PPG) data, and send the PPG data to the cloud server.

[0029] The cloud server receives the PPG data sent by the electronic device, divides the PPG data into multiple PPG data segments, and determines the blood pressure initial value and probability density distribution data corresponding to each PPG data segment based on each PPG data segment, wherein the probability density distribution data is used to represent the probabilities corresponding to different blood pressure initial values.

[0030] After determining the initial blood pressure value and probability density distribution data corresponding to each PPG data segment, the cloud server can determine the effective initial blood pressure value from multiple initial blood pressure values ​​based on the matching relationship between the initial blood pressure value and the probability density distribution data corresponding to each PPG data segment, and determine the blood pressure measurement result based on the effective initial blood pressure value.

[0031] like Figure 3 As shown, in the process of the cloud server determining the blood pressure measurement result based on the PPG data, the second interface 1302 of the electronic watch can display the words "Blood pressure is being measured, please keep still" to prompt the user to keep still to improve the accuracy of the blood pressure measurement result.

[0032] After determining the blood pressure measurement result, the cloud server sends the blood pressure measurement result to the electronic watch, and the corresponding blood pressure measurement result can be displayed in the third interface 1303 of the electronic watch. The blood pressure measurement result can include at least one of a blood pressure value and a blood pressure level.

[0033] It is understandable that the figure only takes the blood pressure measurement results including the blood pressure value as an example, and the blood pressure value includes a low pressure value (diastolic pressure value) and a high pressure value (systolic pressure value).

[0034] The following is an introduction to a physiological signal measurement method provided in an embodiment of the present application. Figure 4 As shown, this figure is a flow chart of a physiological signal measurement method provided in an embodiment of the present application, including S401-S407.

[0035] S401. The electronic device obtains photoplethysmography data.

[0036] In an embodiment of the present application, after a user wears the electronic device, the electronic device can obtain photoplethysmography (PPG) data in response to a measurement operation of the user.

[0037] S402. The electronic device sends the photoplethysmography data to the cloud server.

[0038] After obtaining the PPG data, the electronic device can send the PPG data to the cloud server so that the cloud server processes and analyzes the PPG data to obtain physiological signal measurement results. In the embodiment of the present application, the physiological signal may include but is not limited to blood pressure, heart rate, blood oxygen saturation, etc.

[0039] S403: The cloud server divides the photoplethysmography data into multiple photoplethysmography data segments.

[0040] In an embodiment of the present application, the cloud server can divide a segment of PPG data sent by the electronic device to obtain multiple PPG data segments, so that the initial values ​​of physiological signals of the multiple PPG data segments can be screened subsequently to determine the valid initial values ​​of physiological signals and obtain accurate physiological signal measurement results.

[0041] In one example, the PPG data may be divided according to duration, such as Figure 5 As shown, by dividing a PPG data with a duration of 1 minute (min) and a frequency of 100 Hz, 6 PPG data segments can be obtained, and the duration of each PPG data segment is 10 seconds.

[0042] In another example, the PPG data may be divided according to the cardiac cycle, such as Figure 6As shown, by determining every two cardiac cycles as a PPG data segment, multiple PPG data segments can be obtained. For example, a segment of PPG data containing 10 cardiac cycles is divided to obtain 5 PPG data segments, each of which contains two cardiac cycles.

[0043] It will be appreciated that in the embodiments of the present application, there is no specific limitation on the duration of each PPG data segment or the number of cardiac cycles contained in the PPG data segment, and the above is only an example.

[0044] S404. The cloud server determines the initial value and probability density distribution data of the physiological signal corresponding to each photoplethysmography data segment based on each photoplethysmography data segment.

[0045] Among them, the probability density distribution data is used to represent the probabilities corresponding to the initial values ​​of different physiological signals.

[0046] In the embodiment of the present application, the physiological signal may include but is not limited to blood pressure, heart rate, etc. When the physiological signal is blood pressure, the initial value of the physiological signal corresponding to each photoplethysmography data segment is the blood pressure value corresponding to each photoplethysmography data segment; when the physiological signal is heart rate, the initial value of the physiological signal corresponding to each photoplethysmography data segment is the heart rate value corresponding to each photoplethysmography data segment.

[0047] In one possible implementation, the cloud server can perform physiological signal numerical regression on each PPG data segment based on the regression model to determine the initial value of the physiological signal corresponding to each PPG data segment; based on the classification model, the cloud server can perform physiological signal classification on each PPG data segment to determine the probability density distribution data corresponding to each PPG data segment.

[0048] In the embodiment of the present application, the regression model and the classification model can be trained based on the training set data.

[0049] In a possible implementation, PPG data and standard data may be collected, and the best PPG data segment may be screened from the PPG data, and the best PPG data segment and the corresponding standard data may be used as a training set.

[0050] The standard data is data measured by traditional measuring tools, which can be used as accurate physiological signal data to match with PPG data. For example, when the physiological signal is blood pressure, the standard data can be the blood pressure value measured by a traditional mercury sphygmomanometer.

[0051] like Figure 7As shown, in each round of data collection, 1 minute of PPG data can be collected first, and then the standard data can be measured using traditional measurement tools. After that, in order to improve the accuracy of the next round of data collection, you can rest for 30 seconds after measuring the standard data, and start the next round of data collection after 30 seconds, thereby obtaining multiple PPG data and standard data.

[0052] In order to improve the accuracy of the training data set, each segment of PPG data can be divided into multiple PPG data segments. Figure 5 As shown in FIG. 1 , 6 PPG data segments can be obtained by dividing the 1-minute PPG data into 10-second windows. On this basis, 5 best PPG data segments are selected from the 6 PPG data segments according to the data quality of the PPG data segments. The best PPG data segments obtained through screening and the corresponding standard data are used as training data.

[0053] It will be appreciated that the duration of collecting PPG data, the rest duration, the number of rounds of data collection, the number of PPG segments, and the number of optimal PPG data segments selected from each PPG data are not specifically limited in the embodiments of the present application, and the above are merely examples.

[0054] In another possible implementation, since PPG data is affected by factors such as the environment, user wearing conditions, and exercise conditions, its quality is unstable. For example, in PPG data with a duration of 1 minute, the data in the last 30 seconds is usually closer to the actual measured standard data.

[0055] To improve the accuracy of the training set data, in the embodiment of the present application, the training set data can be generated through steps 411 to 413 as described below.

[0056] Step 411: Acquire PPG data and standard data collected in multiple time periods.

[0057] In the embodiment of the present application, multiple rounds of data collection can be performed during the data collection stage, such as Figure 8 As shown, four rounds of data collection are taken as an example for introduction.

[0058] Each round of data collection process includes 1 minute of PPG data collection, standard data collection, and 30 seconds of rest time, thereby obtaining PPG data and standard data collected in multiple time periods.

[0059] It can be understood that in the embodiments of the present application, there is no specific limitation on the duration of collecting PPG data, the rest duration, and the number of rounds of data collection, and the above are only examples.

[0060] Step 412: Divide the PPG data collected in each time period into a first data segment and a second data segment.

[0061] Among them, the first data segment of the Nth time period is at least one PPG data segment that matches the standard data of the N-1th time period; the second data segment of the Nth time period is at least one PPG data segment that matches the standard data of the Nth time period.

[0062] Step 413: Map the first data segment of the Nth time period with the standard data of the N-1th time period, and map the second data segment of the Nth time period with the standard data of the Nth time period to obtain training set data.

[0063] Wherein, N is a positive integer greater than 0.

[0064] For example, 1 minute of PPG data is divided into 6 PPG data segments, each of which is 10 seconds long. For each PPG data, the PPG data segment of 0-10 seconds can be marked as index 0; the PPG data segment of 11-20 seconds can be marked as index 1; the PPG data segment of 21-30 seconds can be marked as index 2; the PPG data segment of 31-40 seconds can be marked as index 3; the PPG data segment of 41-50 seconds can be marked as index 4; and the PPG data segment of 51-60 seconds can be marked as index 5.

[0065] When the fluctuation of physiological signals before and after resting for 30 seconds remains unchanged or is small, the PPG data segments with indexes 0, 1, and 2 in each PPG data can be used as the first data segments, and the PPG data segments with indexes 3, 4, and 5 in each PPG data can be used as the second data segments.

[0066] When N is 3, the PPG data segments with indices 0, 1, and 2 in the first time period are mapped to the standard data of the 0th time period, and the PPG data segments with indices 3, 4, and 5 in the first time period are mapped to the standard data of the first time period; the PPG data segments with indices 0, 1, and 2 in the second time period are mapped to the standard data of the first time period, and the PPG data segments with indices 3, 4, and 5 in the second time period are mapped to the standard data of the second time period; the PPG data segments with indices 0, 1, and 2 in the third time period are mapped to the standard data of the second time period, and the PPG data segments with indices 3, 4, and 5 in the third time period are mapped to the standard data of the third time period, to obtain the training set data.

[0067] In an embodiment of the present application, training set data is obtained by mapping the first data segment of the Nth time period with the standard data of the N-1th time period, and mapping the second data segment of the Nth time period with the standard data of the Nth time period. The training set data collection process is optimized from the time scale to facilitate the subsequent improvement of the accuracy of the physiological signal detection results.

[0068] In a possible implementation, based on the classification model, each PPG data segment is classified as a physiological signal, and the probability density distribution data corresponding to each PPG data segment obtained is Gaussian distributed. In this case, during the training process of the classification model, the training set data can be used as input to output the probability density distribution data corresponding to the training set data. The mean value of the probability density distribution data is determined based on the standard data in the training set data.

[0069] like Fig. 9 As shown, data can be classified based on soft label or hard label classification. Taking the physiological signal as blood pressure value as an example, based on the distribution of training set data, the hard label classification method can determine that the probability of the diastolic blood pressure value being within the range of 90mmHg-100mmHg is 1, and the probability of being outside the range is 0.

[0070] Compared with the hard label classification method, the soft label classification method has a certain degree of fault tolerance, such as Fig.10 As shown, based on the distribution of the training set data, the soft label classification method can determine that the probability that the diastolic blood pressure value is in the range of 80 mmHg-90 mmHg is 0.3, the probability that it is in the range of 90 mmHg-100 mmHg is 0.7, and the probability that it is in other ranges is 0.

[0071] However, the methods of classifying physiological signals based on soft labels or hard labels all have the problem of poor continuity in interval distribution. In order to reduce the difficulty of the classification task and improve the accuracy of classification, Gaussian labels are used to classify physiological signals in the embodiments of the present application.

[0072] In the embodiment of the present application, based on the distribution of the training set data, the probability interval window length corresponding to the physiological signal can be determined, and the probability interval of the physiological signal can be divided. Based on the statistical results of the training set data and the blood pressure acceptance criteria, the mean and variance of the Gaussian curve can be determined to generate probability density distribution data with Gaussian distribution, and the steps are as follows: Step 421: Define input parameters.

[0073] The input parameters include the probability intervals (intervals), given values ​​(sample point) and standard deviation (std) of the physiological signal.

[0074] Taking blood pressure as an example, the length of the probability interval window corresponding to the physiological signal is determined based on the distribution of the training set data, and the probability interval of the physiological signal is obtained by dividing it. The given value is determined according to the standard data in the training set data, and the standard deviation of the Gaussian curve is set.

[0075] like Fig.11 As shown, the probability interval window length corresponding to the physiological signal can be 10 mm Hg, and the probability intervals of the physiological signal obtained by division can include (80, 90), (90, 100), ..., (150, 160), etc. Given a value of 92 mm Hg, the standard deviation of the Gaussian curve can be set to 10 / 3.

[0076] Step 422: Initialize the mean.

[0077] Assign the given value to the mean. Exemplarily, based on the standard data in the training set data, the mean of the standard data can be determined, used as the given value, and the mean is initialized based on the given value.

[0078] Step 423: Initialize the probability list.

[0079] Create an empty list and name it probabilities.

[0080] Step 424: traverse the probability intervals of the physiological signal, determine the probability corresponding to each probability interval of the physiological signal and add it to the probability list.

[0081] For each probability interval of a physiological signal, extract the start value and end value of the probability interval of the physiological signal, and calculate the Cumulative Distribution Function (CDF) of the start value and the end value, as shown in formula (1): cdf_start=norm.cdf(start,loc=mean,scale=std) cdf_end=norm.cdf(end,loc=mean,scale=std) (1) Among them, the norm.cdf function is used to calculate the cumulative distribution function value of the normal distribution, where the cumulative distribution function is used to represent the probability that a random variable takes a value at or below a certain value; cdf_start is a variable used to store the cumulative distribution function value corresponding to the starting value, which is the probability that the random variable is less than or equal to the starting value; cdf_end is a variable used to store the cumulative distribution function value corresponding to the end value, which is the probability that the random variable is less than or equal to the end value; loc is a keyword parameter in the norm.cdf function, loc=mean, which means that loc specifies the mean of the normal distribution. In the normal distribution, the mean is the center point of the distribution and determines the location of the distribution; scale is another keyword parameter in the norm.cdf function, scale=std, which means that scale specifies the standard deviation of the normal distribution. The standard deviation determines the width or dispersion of the distribution. The larger the standard deviation, the wider the distribution, and the smaller the standard deviation, the narrower the distribution.

[0082] The probability corresponding to the probability interval of the physiological signal can be expressed by formula (2) as follows: probability=cdf_end-cdf_start (2) Wherein, probability represents the probability corresponding to the probability interval of the physiological signal. After obtaining the probability corresponding to the probability interval of each physiological signal, the probability corresponding to the probability interval of each physiological signal can be added to the probability list.

[0083] Step 425: Obtain probability density distribution data based on the probability list.

[0084] In the embodiment of the present application, in order to improve data processing efficiency, the probability density function with Gaussian distribution can be obtained by converting probabilities into a NumPy array and returning it.

[0085] In the embodiment of the present application, probability intervals are used to classify physiological signals, and probability density distribution data showing a Gaussian distribution is used to represent the probabilities corresponding to different initial values ​​of physiological signals, thereby reducing the difficulty of the physiological signal classification task.

[0086] S405. The cloud server determines a valid physiological signal initial value from a plurality of physiological signal initial values ​​according to a matching relationship between the physiological signal initial value corresponding to each photoplethysmography data segment and the probability density distribution data.

[0087] In a possible implementation, the cloud server can determine the probability interval corresponding to each physiological signal initial value according to the matching relationship between each physiological signal initial value corresponding to each PPG data segment and the probability density distribution data.

[0088] For example, Fig.11 As shown, the central value of the probability density distribution data is 92. If the initial value of the physiological signal is between 90-100, the probability interval corresponding to the initial value of the physiological signal is (90, 100); if the initial value of the physiological signal is between 80-90, the probability interval corresponding to the initial value of the physiological signal is (80, 90), and so on.

[0089] When the probability interval corresponding to a physiological signal initial value is the first interval or the second interval, the cloud server can determine the physiological signal initial value as a valid physiological signal initial value; when the probability interval corresponding to a physiological signal initial value is the third interval, the cloud server can determine the physiological signal initial value as an invalid physiological signal initial value.

[0090] The integral value of the probability density distribution data in the first interval is the highest, the integral value in the second interval is smaller than the integral value in the first interval, and is larger than the integral value in the third interval.

[0091] For example, Fig.11 As shown, the integral value of the probability density distribution data in the probability interval of 90-100 is the highest, and the integral value in the probability interval of 80-90 is smaller than the integral value in the probability interval of 90-100, and larger than the integral value in other probability intervals. (90, 100) is the first interval, (80, 90) is the second interval, and the third interval is the other probability intervals except the first interval and the second interval. When the probability interval corresponding to the initial value of the physiological signal is (80, 100), the cloud server can determine the initial value of the physiological signal as a valid initial value of the physiological signal; when the probability interval corresponding to the initial value of the physiological signal is not within the range of (80, 100), the cloud server can determine the initial value of the physiological signal as an invalid initial value of the physiological signal.

[0092] The range of (80, 100) may include at least one of 80 and 100, or may not include both.

[0093] It can be understood that, in the above examples, the values ​​of 80, 90, 100, etc. are used as the boundary values ​​of each interval, and the probability intervals to which each boundary value belongs can be set as needed, for example, the first interval is set to include [90, 100], and the second interval is set to include [80, 90), that is, 80 belongs to the second interval, 90 belongs to the first interval, and 100 belongs to the third interval; or the first interval is set to include (90, 100], and the second interval is set to include (80, 90], that is, 80 belongs to the third interval, 90 belongs to the second interval, and 100 belongs to the first interval; or the first interval is set to include [90, 100], and the second interval is set to include [80, 90], that is, 80 belongs to the second interval, 90 belongs to the first interval and the second interval, and 100 belongs to the first interval.

[0094] In a possible implementation, the cloud server determines the probability of the physiological signal initial value based on the matching relationship between each physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, and can then determine whether the physiological signal initial value is a valid physiological signal initial value or an invalid physiological signal initial value based on the size relationship between the physiological signal initial value and the first threshold.

[0095] Exemplarily, when the probability of a physiological signal initial value is greater than a first threshold, the cloud server determines the physiological signal initial value as a valid physiological signal initial value; when the probability of a physiological signal initial value is less than or equal to the first threshold, the cloud server determines the physiological signal initial value as an invalid physiological signal initial value.

[0096] It is understandable that in the embodiment of the present application, there is no specific limitation on the size of the first threshold, which can be set according to actual needs.

[0097] S406: The cloud server determines the physiological signal measurement result based on the effective physiological signal initial value.

[0098] In the embodiment of the present application, the physiological signal measurement result may include, but is not limited to, at least one of a specific physiological signal value and a level corresponding to the physiological signal value.

[0099] Exemplarily, the specific physiological signal value may be a blood pressure value or a heart rate value, etc., and the level corresponding to the physiological signal value may be normal blood pressure, high blood pressure, normal heart rate, high heart rate, etc.

[0100] In a possible implementation, when there are multiple valid physiological signal initial values, the cloud server may determine an average of the multiple valid physiological signal initial values ​​as the physiological signal value, and determine the physiological signal measurement result based on the physiological signal value.

[0101] In one example, the cloud server may directly use the physiological signal value as the physiological signal measurement result.

[0102] In another example, the cloud server may determine the level corresponding to the physiological signal value based on the physiological signal value, and determine the physiological signal value and / or level as the physiological signal measurement result.

[0103] In order to improve the accuracy of the physiological signal measurement results in the embodiment of the present application, the cloud server can process the valid physiological signal initial value according to the probability interval corresponding to the valid physiological signal initial value, and then determine the physiological signal measurement result.

[0104] In a possible implementation, when all valid physiological signal initial values ​​correspond to the first interval, the cloud server may determine the physiological signal measurement result based on the average of each valid physiological signal initial value.

[0105] In the embodiment of the present application, when the effective physiological signal initial value corresponds to the first interval, it means that the credibility of the effective physiological signal initial value is high, and the physiological signal value can be directly determined based on the effective physiological signal initial value of the first interval.

[0106] For example, three valid physiological signal initial values, a1, a2, and a3, are determined from a plurality of physiological signal initial values, wherein a1, a2, and a3 all correspond to the first interval. Based on a1, a2, and a3, the physiological signal value can be determined as (a1, a2, a3) / 3 by taking the average of the valid physiological signal initial values.

[0107] After determining the physiological signal value, the cloud server may directly use the physiological signal value as the physiological signal measurement result; or determine the level corresponding to the physiological signal value based on the physiological signal value, and determine the physiological signal value and / or level as the physiological signal measurement result.

[0108] In one possible implementation, when all valid physiological signal initial values ​​correspond to the second interval, it indicates that the credibility of the valid physiological signal initial values ​​is high. To improve the accuracy of the physiological signal measurement signal, the cloud server can determine the probability of each valid physiological signal initial value based on the probability density distribution data.

[0109] By multiplying each valid physiological signal initial value by the probability of each valid physiological signal initial value, a weighted physiological signal initial value corresponding to each valid physiological signal initial value is determined. The cloud server can determine the physiological signal measurement result based on the average of each weighted physiological signal initial value.

[0110] The probability of the initial value of the effective physiological signal is the output value obtained by taking the initial value of the effective physiological signal as the input value of the probability density distribution function.

[0111] Exemplarily, three valid physiological signal initial values ​​are determined from a plurality of physiological signal initial values, namely b1, b2, and b3, wherein b1, b2, and b3 all correspond to the second interval.

[0112] Based on the probability density distribution data, the probability of b1 is determined to be w1, the probability of b2 is w2, and the probability of b3 is w3. Then, the initial value of the weighted physiological signal corresponding to b1 is b1×w1; the initial value of the weighted physiological signal corresponding to b2 is b2×w2; the initial value of the weighted physiological signal corresponding to b3 is b3×w3. After obtaining the initial values ​​of each weighted physiological signal, the physiological signal value can be determined to be the average of the initial values ​​of each weighted physiological signal, that is, (b1×w1+b2×w2+b3×w3) / 3.

[0113] After determining the physiological signal value, the cloud server may directly use the physiological signal value as the physiological signal measurement result; or determine the level corresponding to the physiological signal value based on the physiological signal value, and determine the physiological signal value and / or level as the physiological signal measurement result.

[0114] In a possible implementation, when the effective physiological signal initial values ​​include the effective physiological signal initial values ​​corresponding to the first interval and the effective physiological signal initial values ​​corresponding to the second interval, the cloud server may determine the weighted physiological signal initial value corresponding to each effective physiological signal initial value by multiplying the probability of each effective physiological signal initial value with the effective physiological signal initial value. On this basis, the cloud server may determine the physiological signal measurement result based on the weighted physiological signal initial value of the effective physiological signal initial value corresponding to the second interval and the average of the effective physiological signal initial value corresponding to the first interval.

[0115] Exemplarily, three valid physiological signal initial values ​​are determined from multiple physiological signal initial values ​​as c1, c2, and c3, where c1 corresponds to the first interval, and c2 and c3 correspond to the second interval. Based on the probability density distribution function, the probability of c2 can be determined to be w4, and the probability of c3 can be determined to be w5.

[0116] On this basis, the cloud server can determine that the initial value of the weighted physiological signal corresponding to c2 is c2×w4, and the initial value of the weighted physiological signal corresponding to c3 is c3×w5. Therefore, the physiological signal value can be determined to be (c1+c2×w4+c3×w5) / 3.

[0117] After determining the physiological signal value, the cloud server may directly use the physiological signal value as the physiological signal measurement result; or determine the level corresponding to the physiological signal value based on the physiological signal value, and determine the physiological signal value and / or level as the physiological signal measurement result.

[0118] S407: The electronic device receives the physiological signal measurement result sent by the cloud server and displays it.

[0119] In the embodiment of the present application, the physiological signal measurement result may include at least one of a physiological signal value and a level corresponding to the physiological signal value. After receiving the physiological signal measurement result sent by the cloud server, the electronic device may display it on a display screen.

[0120] In summary, in the embodiment of the present application, according to the matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, the physiological signal initial value with low confidence can be filtered out, and the effective physiological signal initial value can be determined from multiple physiological signal initial values, and the physiological signal measurement result can be determined based on the effective physiological signal initial value. The physiological signal measurement method provided in the embodiment of the present application determines the physiological signal value based on the physiological signal initial value with high confidence, which can effectively improve the accuracy of the physiological signal value.

[0121] For a better understanding, the physiological signal measurement method provided in the embodiment of the present application is introduced below by taking blood pressure as an example of the physiological signal. Figure 12-13 As shown, Fig.12 A schematic diagram of a blood pressure measurement method provided in an embodiment of the present application, Fig.13 A schematic diagram of a physiological signal measurement algorithm provided in an embodiment of the present application.

[0122] In an embodiment of the present application, when the physiological signal is blood pressure, the initial value of the physiological signal, that is, the blood pressure value corresponding to each PPG data segment includes an initial systolic pressure value and an initial diastolic pressure value; the physiological signal value includes a systolic pressure value and a diastolic pressure value; the probability density distribution data includes first distribution data corresponding to the initial systolic pressure value and second distribution data corresponding to the initial diastolic pressure value.

[0123] The electronic device obtains PPG data and sends the PPG data to a cloud server, which divides the PPG data into multiple PPG data segments.

[0124] It can be understood that the specific method is the same as S401-S403 above, and will not be repeated here.

[0125] The cloud server inputs each photoplethysmography data segment into the blood pressure dual-branch model to determine the blood pressure initial value and probability density distribution data corresponding to each photoplethysmography data segment. The blood pressure dual-branch model includes a regression model and a classification model, and the blood pressure initial value includes the systolic pressure initial value and the diastolic pressure initial value.

[0126] like Figure 2 or Figure 6 As shown, in each PPG data segment, the peak indicates that the heart is in a contraction state, and the trough indicates that the heart is in a diastole state. Based on this, the cloud server can determine the initial systolic pressure value and the initial diastolic pressure value corresponding to each PPG segment based on the PPG data segment.

[0127] For the initial systolic pressure value and the initial diastolic pressure value, the first distribution data corresponding to the initial systolic pressure value and the second distribution data corresponding to the initial diastolic pressure value can be obtained based on the method in S404.

[0128] The cloud server determines an effective initial systolic pressure value from a plurality of initial systolic pressure values ​​according to the initial systolic pressure value corresponding to each PPG data segment and the first distribution data. The cloud server determines an effective initial diastolic pressure value from a plurality of initial diastolic pressure values ​​according to the initial diastolic pressure value corresponding to each PPG data segment and the second distribution data.

[0129] The cloud server determines the mean of the effective systolic pressure initial value as the systolic pressure value, and determines the effective diastolic pressure initial value as the diastolic pressure value. On this basis, the physiological signal measurement result can be determined based on the systolic pressure value and the diastolic pressure value.

[0130] In the embodiment of the present application, the physiological signal measurement result may include but is not limited to at least one of a specific blood pressure value and a level corresponding to the blood pressure value.

[0131] In one example, the cloud server may directly use the systolic blood pressure value and the diastolic blood pressure value as the physiological signal measurement results.

[0132] In another example, the cloud server may determine the level corresponding to the physiological signal value based on the systolic blood pressure value and the diastolic blood pressure value, and determine the systolic blood pressure value and the diastolic blood pressure value and / or their respective corresponding levels as the physiological signal measurement result.

[0133] As shown in Table 1 below, the table shows the corresponding levels of different systolic and diastolic blood pressure values. When the systolic blood pressure value is less than 120 mm Hg and the diastolic blood pressure value is less than 80 mm Hg, the blood pressure level is normal blood pressure; when the systolic blood pressure value is between 120 mm Hg and 129 mm Hg, and the diastolic blood pressure value is less than 80 mm Hg, the blood pressure level is elevated blood pressure; when the systolic blood pressure value is between 130 mm Hg and 139 mm Hg, or the diastolic blood pressure value is between 80 mm Hg and 89 mm Hg, the blood pressure level is stage 1 hypertension; when the systolic blood pressure value is greater than or equal to 140 mm Hg, or the diastolic blood pressure value is greater than or equal to 90 mm Hg, the blood pressure level is stage 2 hypertension.

[0134] Table 1

[0135] It can be understood that the levels corresponding to the above-mentioned different systolic blood pressure values ​​and diastolic blood pressure values ​​are only examples.

[0136] After the cloud server determines the physiological signal measurement result, it can send the physiological signal measurement result to the electronic device. After receiving the physiological signal measurement result sent by the cloud server, the electronic device can display it.

[0137] In summary, in the embodiment of the present application, according to the matching relationship between the initial systolic pressure value and the initial diastolic pressure value corresponding to each PPG data segment and the first distribution data and the second distribution data, the initial systolic pressure value and the initial diastolic pressure value with low confidence can be filtered out, and the effective initial systolic pressure value and the effective initial diastolic pressure value can be determined from multiple initial systolic pressure values ​​and initial diastolic pressure values. Based on the effective initial systolic pressure value and the effective initial diastolic pressure value, the physiological signal measurement result is determined.

[0138] It can be understood that the method provided in the embodiment of the present application can also be applied to the measurement of physiological signals such as heart rate and blood oxygen saturation. The specific method is similar to that of blood pressure measurement and will not be repeated here.

[0139] In some embodiments, the cloud server can determine at least one of the physiological signals such as blood pressure value, heart rate value, and blood oxygen saturation based on the PPG data, and determine the physiological signal measurement result based on at least one of the physiological signals such as blood pressure value, heart rate value, and blood oxygen saturation.

[0140] For example, when the cloud server determines the blood pressure value and the heart rate value based on the PPG data, the physiological signal measurement result may include the blood pressure value and the heart rate value; or include the blood pressure value, the level of the blood pressure value, the heart rate value and the level of the heart rate value. Thus, after the electronic device obtains the physiological signal measurement result sent by the cloud server, it can simultaneously display the relevant measurement results of the blood pressure and the heart rate.

[0141] The physiological signal measurement method provided in the embodiment of the present application determines the physiological signal value based on the physiological signal initial value with a higher confidence level, which can effectively improve the accuracy of the physiological signal value, and further improve the accuracy of the physiological signal measurement result.

[0142] The electronic device in the embodiment of the present application may be a wearable device, such as an electronic watch, an electronic bracelet, and other electronic clothing. The embodiment of the present application does not limit the specific technology and specific device form used by the electronic device.

[0143] In order to better understand the embodiments of the present application, the structure of the electronic device in the embodiments of the present application is introduced below. Fig.14 A schematic diagram of the hardware structure of an electronic device is shown.

[0144] The electronic device may include a processor 1410 , a mobile communication module 1420 , a wireless communication module 1421 , a sensor module 1430 , a button 1440 , a display screen 1450 , and the like.

[0145] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0146] The processor 1410 may include one or more processing units, for example, the processor 1410 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), etc. Different processing units may be independent devices or integrated into one or more processors.

[0147] The controller can generate operation control signals according to the instruction operation code and timing signal to complete the control of instruction fetching and execution.

[0148] The processor 1410 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 1410 is a cache memory. The memory may store instructions or data that the processor 1410 has just used or cyclically used. If the processor 1410 needs to use the instruction or data again, it may be called from the memory. This avoids repeated access, reduces the waiting time of the processor 1410, and thus improves the efficiency of the system.

[0149] In the embodiment of the present application, the processor 1410 can be used to obtain PPG data segments and send the PPG data segments to the cloud server. The cloud server divides the PPG data into multiple PPG data segments, and determines the physiological signal initial value and probability density distribution data corresponding to each PPG data segment based on each PPG data segment; determines the effective physiological signal initial value from the multiple physiological signal initial values ​​according to the matching relationship between the physiological signal initial value and the probability density distribution data corresponding to each PPG data segment; after determining the physiological signal measurement result based on the effective physiological signal initial value, the processor displays the physiological signal measurement result according to the physiological signal measurement result sent by the cloud server.

[0150] The wireless communication function of the electronic device can be implemented through antenna 1, antenna 2, mobile communication module 1420, wireless communication module 1421, modem processor and baseband processor.

[0151] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Antennas in electronic devices can be used to cover single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0152] The mobile communication module 1420 can provide solutions for wireless communications including 2G / 3G / 4G / 5G, etc., applied to electronic devices. The mobile communication module 1420 can include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 1420 can receive electromagnetic waves from the antenna 1, and filter, amplify, etc. the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation.

[0153] The wireless communication module 1421 can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0154] The wireless communication module 1421 may be one or more devices integrating at least one communication processing module. The wireless communication module 1421 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signal and performs filtering, and sends the processed signal to the processor 1410. The wireless communication module 1421 may also receive a signal to be sent from the processor 1410, modulate the signal, amplify the signal, and convert it into an electromagnetic wave for radiation via the antenna 2.

[0155] In some embodiments, the antenna 1 of the electronic device is coupled to the mobile communication module 1420, and the antenna 2 is coupled to the wireless communication module 1421, so that the electronic device can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology, etc. GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (QZSS) and / or satellite based augmentation system (SBAS).

[0156] Based on this, in an embodiment of the present application, the electronic device can communicate with a cloud server, and after obtaining PPG data based on the sensor, it can send the PPG data to the cloud server and receive the physiological signal measurement results sent by the cloud server. After obtaining the physiological signal measurement results, the physiological signal measurement results are displayed on the display screen 1450 through the GPU, the display screen 1450, and the application processor.

[0157] Exemplarily, a GPU is a microprocessor for image processing, connected to the display screen 1450 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 1410 may include one or more GPUs that execute program instructions to generate or change display information.

[0158] The display screen 1450 includes a display panel for displaying images, videos, etc.

[0159] The display panel can adopt liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active matrix organic light-emitting diode (AMOLED), flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diodes (QLED), etc.

[0160] In some embodiments, the electronic device may include 1 or N display screens 1450 , where N is a positive integer greater than 1.

[0161] The software system of the electronic device may adopt a layered architecture, an event-driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture, etc. The embodiment of the present application takes the Android system of the layered architecture as an example to exemplify the software structure of the electronic device.

[0162] Fig.15 A software structure block diagram of an electronic device provided in an embodiment of the present application.

[0163] The layered architecture divides the software into several layers, each with clear roles and division of labor. The layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, from top to bottom: the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.

[0164] The application layer can include a series of application packages.

[0165] like Fig.15 As shown, the application package may include applications such as blood pressure measurement, heart rate measurement, and blood oxygen saturation measurement.

[0166] The application framework layer provides application programming interface (API) and programming framework for the applications in the application layer. The application framework layer includes some predefined functions. Fig.15 As shown, the application framework layer may include a view system, a resource manager, a notification manager, and the like.

[0167] The view system includes visual controls, such as controls for displaying text, controls for displaying images, etc. The view system can be used to build applications. A display interface can be composed of one or more views. For example, a display interface including a text notification icon can include a view for displaying text and a view for displaying images.

[0168] The resource manager provides various resources for applications, such as localized strings, icons, images, layout files, video files, and so on.

[0169] The notification manager enables applications to display notification information in the status bar. It can be used to convey notification-type messages and can disappear automatically after a short stay without user interaction. For example, the notification manager is used to notify the completion of a measurement, prompt the user that a physiological signal test is being performed, etc.

[0170] Android Runtime includes core libraries and virtual machines. Android runtime is responsible for scheduling and management of the Android system.

[0171] The core library consists of two parts: one part is the function that needs to be called by the Java language, and the other part is the Android core library.

[0172] The application layer and the application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and the application framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.

[0173] The system library may include multiple functional modules, such as a 3D graphics processing library (eg OpenGL ES), a 2D graphics engine (eg SGL), etc.

[0174] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0175] A 2D graphics engine is a drawing engine for 2D drawings.

[0176] The kernel layer is the layer between hardware and software. The kernel layer contains at least display drivers, sensor drivers, etc.

[0177] It should be noted that although the embodiments of the present application are described using the Android system as an example, its basic principles are also applicable to electronic devices based on operating systems such as iOS and Windows.

[0178] This embodiment further provides a computer-readable storage medium, which includes instructions. When the instructions are executed on an electronic device, the electronic device executes the above-mentioned related method steps to implement the method in the above-mentioned embodiment.

[0179] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A physiological signal measurement method, characterized in that: include: Based on each photoplethysmography PPG data segment, determining an initial value of a physiological signal and probability density distribution data corresponding to each PPG data segment; each PPG data segment is obtained by dividing the PPG data, and the probability density distribution data is used to represent the probabilities corresponding to different initial values ​​of the physiological signal; Determine a valid physiological signal initial value from a plurality of physiological signal initial values ​​according to a matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data; Based on the effective physiological signal initial value, a physiological signal measurement result is determined.

2. The method according to claim 1, characterized in that The determining of a valid physiological signal initial value from a plurality of physiological signal initial values ​​according to a matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data comprises: Determine the probability interval corresponding to each physiological signal initial value according to the matching relationship between each physiological signal initial value corresponding to each PPG data segment and the probability density distribution data; When the probability interval corresponding to a physiological signal initial value is the first interval or the second interval, the physiological signal initial value is determined as a valid physiological signal initial value; when the probability interval corresponding to a physiological signal initial value is the third interval, the physiological signal initial value is determined as an invalid physiological signal initial value; the integral value of the probability density distribution data in the first interval is the highest, the integral value in the second interval is smaller than the integral value in the first interval, and greater than the integral value in the third interval.

3. The method according to claim 2, characterized in that The effective physiological signal initial values ​​all correspond to the second interval, and determining the physiological signal measurement result based on the effective physiological signal initial values ​​includes: Based on the probability density distribution data, determining the probability of each valid physiological signal initial value; Determine the weighted physiological signal initial value corresponding to each valid physiological signal initial value by multiplying the product of each valid physiological signal initial value and the probability of each valid physiological signal initial value; The physiological signal measurement result is determined based on the average of the initial values ​​of each weighted physiological signal.

4. The method according to claim 2, characterized in that: The effective physiological signal initial value includes an effective physiological signal initial value corresponding to the first interval and an effective physiological signal initial value corresponding to the second interval, and determining the physiological signal measurement result based on the effective physiological signal initial value includes: For the effective physiological signal initial value corresponding to the second interval, the product of each effective physiological signal initial value and the probability of the effective physiological signal initial value is determined as a weighted physiological signal initial value corresponding to each effective physiological signal initial value; the probability of the effective physiological signal initial value is determined based on the probability density distribution data; A physiological signal measurement result is determined based on the weighted physiological signal initial value corresponding to the second interval of the effective physiological signal initial value and the average of the effective physiological signal initial values ​​corresponding to the first interval.

5. The method according to claim 1, characterized in that The determining of a valid physiological signal initial value from a plurality of physiological signal initial values ​​according to a matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data comprises: Determine the probability of the initial value of the physiological signal according to the matching relationship between each initial value of the physiological signal corresponding to each PPG data segment and the probability density distribution data; When the probability of a physiological signal initial value is greater than the first threshold, the physiological signal initial value is determined as a valid physiological signal initial value; when the probability of a physiological signal initial value is less than or equal to the first threshold, the physiological signal initial value is determined as an invalid physiological signal initial value.

6. The method according to any one of claims 1 to 5, characterized in that: The method of determining the physiological signal initial value and probability density distribution data corresponding to each PPG data segment based on each photoplethysmography PPG data segment includes: Based on the regression model, perform physiological signal numerical regression on each PPG data segment to determine the initial value of the physiological signal corresponding to each PPG data segment; Based on the classification model, physiological signal classification is performed on each PPG data segment to determine the probability density distribution data corresponding to each PPG data segment; the regression model and the classification model are obtained by training based on the training set data; The generation process of the training set data includes: Obtain PPG data and standard data collected in multiple time periods; Dividing the PPG data collected in each time period into a first data segment and a second data segment; The first data segment of the Nth time period is mapped with the standard data of the N-1th time period, and the second data segment of the Nth time period is mapped with the standard data of the Nth time period to obtain training set data, where N is a positive integer greater than 0.

7. The method according to claim 6, characterized in that The probability density distribution data is Gaussian distributed, and the training steps of the classification model include: The training set data is used as input, and probability density distribution data corresponding to the training set data is output, wherein the mean value of the probability density distribution data is determined based on the standard data in the training set data.

8. The method according to any one of claims 1 to 5, characterized in that: When the physiological signal is blood pressure, the physiological signal initial value includes a systolic pressure initial value and a diastolic pressure initial value, the probability density distribution data includes first distribution data corresponding to the systolic pressure initial value and second distribution data corresponding to the diastolic pressure initial value, and determining a valid physiological signal initial value from a plurality of physiological signal initial values ​​according to a matching relationship between the physiological signal initial value corresponding to each PPG data segment and the probability density distribution data, comprises: Determine an effective initial systolic pressure value from the plurality of initial systolic pressure values ​​according to the initial systolic pressure value corresponding to each PPG data segment and the first distribution data, and determine an effective initial diastolic pressure value from the plurality of initial diastolic pressure values ​​according to the initial diastolic pressure value corresponding to each PPG data segment and the second distribution data; The step of determining the physiological signal measurement result based on the effective physiological signal initial value comprises: The mean of the effective initial systolic blood pressure values ​​is determined as the systolic blood pressure value, and the effective initial diastolic blood pressure value is determined as the diastolic blood pressure value; Based on the systolic pressure value and the diastolic pressure value, a physiological signal measurement is determined.

9. The method according to any one of claims 1 to 5, characterized in that: Before determining the physiological signal initial value and probability density distribution data corresponding to each PPG data segment based on each photoplethysmography PPG data segment, the method further includes: Receive PPG data sent by wearable devices; After determining the physiological signal measurement result based on the effective physiological signal initial value, the method further includes: The physiological signal measurement result is sent to the wearable device so that the physiological signal measurement result is displayed on a display interface of the wearable device.

10. The method according to any one of claims 1 to 5, characterized in that: The step of determining the physiological signal measurement result based on the effective physiological signal initial value comprises: When there are multiple valid physiological signal initial values, determining an average of the multiple valid physiological signal initial values ​​as the physiological signal value; Based on the physiological signal value, determining a level corresponding to the physiological signal value; The physiological signal value and / or the level is determined as a physiological signal measurement result.

11. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the steps of a physiological signal measurement method according to any one of claims 1 to 10 according to the instructions in the program code.

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