Physiological detection signal quality evaluation method, electronic device, and storage medium

Abstract

The application provides a physiological detection signal quality evaluation method, an electronic device and a storage medium. The method comprises the following steps: acquiring sample data collected by a detection device in different scenes; selecting sample data of a scene from the sample data set, and dividing the sample data into physiological data and PPG data; calculating an energy proportion of the physiological data according to the physiological data and the PPG data; calculating quality evaluation parameters of the sample data of multiple scenes respectively according to the energy proportions of the physiological data corresponding to different scenes; and evaluating the quality of a physiological detection signal collected by the detection device according to the quality evaluation parameters. The application can quantify the quality of the physiological detection signal, and based on the quantified data, the quality of the physiological detection signal can be conveniently evaluated and adapted to different application scenes, so that the multi-scene performance of the hardware can be conveniently evaluated.

Description

Technical Field

[0001] This application relates to the field of medical testing technology, and in particular to a method for evaluating the quality of physiological testing signals, an electronic device, and a storage medium. Background Technology

[0002] Currently, intelligent detection devices are increasingly widely used, with medical detection primarily based on photoplethysmography (PPG). PPG is a non-invasive method that uses photoelectric means to detect changes in vascular volume in living tissue. The raw PPG signal typically includes various physiological information such as heart rate, blood oxygen saturation, and blood pressure. Because detection devices with PPG measurement capabilities are small, portable, and easy to use, they are widely used for all-weather monitoring of various physiological indicators. However, this all-weather monitoring often involves various complex usage scenarios. These complex scenarios mean that PPG signals are susceptible to complex and variable noise interference, making it difficult to standardize and quantify signal quality across different scenarios and hindering the evaluation of hardware performance in various situations. Summary of the Invention

[0003] In view of the above, it is necessary to provide a physiological detection signal quality assessment method, electronic device and storage medium to solve the problem that signal quality in multiple scenarios cannot be standardized, quantified and assessed.

[0004] Firstly, this application provides a method for evaluating the quality of physiological detection signals. The method includes: acquiring sample datasets collected by a detection device under different scenarios; selecting sample data from a specific scenario within the sample dataset and dividing the sample data into physiological data and PPG data; calculating the energy percentage of the physiological data based on the physiological data and PPG data; calculating quality evaluation parameters for sample data from multiple classification scenarios based on the energy percentage of the physiological data corresponding to different scenarios; and evaluating the quality of the physiological detection signals collected by the detection device based on the quality evaluation parameters. The physiological detection signal quality evaluation method provided in this application quantifies the quality of physiological detection signals collected by the device under different scenarios by calculating the energy percentage of the physiological data, thereby facilitating the evaluation and analysis of signal quality.

[0005] In one possible implementation, acquiring sample data collected by the detection device in different scenarios includes: setting multiple preset scenarios based on labels; connecting the detection device to a data acquisition terminal; generating a single sample data set by collecting data from the detection device based on a single preset scenario; and outputting a multi-scenario sample dataset based on sample data from multiple preset scenarios. This technical solution allows for convenient and accurate collection of sample data from different scenarios.

[0006] In one possible implementation, calculating the energy percentage of physiological data based on physiological data and PPG data includes: calculating multiple energy percentages for corresponding frequencies of multiple physiological data points based on both physiological data and PPG data; and calculating the average of these multiple energy percentages for corresponding frequencies of multiple physiological data points as the energy percentage of the physiological data. This technical solution improves the accuracy of calculating the energy percentage of physiological data by using an averaging method.

[0007] In one possible implementation, calculating multiple energy percentages corresponding to multiple physiological data frequencies based on physiological data and PPG data includes: performing a short-time Fourier transform on the PPG data to obtain the power spectral density data of the PPG data, wherein the window type of the short-time Fourier transform is a Hamming window; determining the time points corresponding to the center positions of multiple Hamming windows, and obtaining the physiological data corresponding to each time point; converting the physiological data into a frequency range; calculating the energy percentage corresponding to the frequency range based on the power spectral density data; and outputting a list of energy percentages of multiple physiological data in the sample data based on the energy percentages corresponding to multiple frequency ranges. This technical solution improves the accuracy of calculating the energy percentages of physiological data through frequency domain analysis.

[0008] In one possible implementation, calculating the energy percentage corresponding to a frequency range based on power spectral density data includes: dividing the sum of the energy values ​​corresponding to multiple frequency values ​​within the frequency range by the sum of the energy values ​​corresponding to all frequency values ​​to obtain the energy percentage corresponding to the frequency range. This technical solution improves the calculation accuracy by using the power spectral density curve obtained through frequency domain analysis to calculate the energy percentage of physiological data.

[0009] In one possible implementation, calculating multiple energy proportions at frequencies corresponding to multiple physiological data points based on physiological data and PPG data further includes interpolating the power spectral density data using time-domain zero-padding or frequency-domain padding. This technical approach can increase frequency domain resolution, thereby improving the accuracy of energy proportion calculations.

[0010] In one possible implementation, calculating the energy percentages of multiple frequencies corresponding to multiple physiological data points based on physiological data and PPG data further includes deleting outlier data from the energy percentage lists of multiple physiological data points. This technical solution can correct calculation biases in energy percentages and improve sample validity.

[0011] In one possible implementation, deleting outlier data from the energy percentage list of multiple physiological data includes: calculating the upper quartile Q1E and lower quartile Q3E in the energy percentage list; calculating the upper threshold U1 and lower threshold U2 for outlier data; identifying data in the energy percentage list that are less than or equal to the lower threshold or greater than or equal to the upper threshold as outlier data; and outputting the energy percentage list after deleting outlier data. This technical solution uses thresholds to determine the boundaries of normal samples, thereby accurately deleting outlier data.

[0012] In one possible implementation, the upper limit threshold lower threshold The above technical solution determines the threshold by setting a preset and adjustable weight or coefficient, making the threshold adjustable to adapt to different application scenarios.

[0013] In one possible implementation, the quality assessment parameters include the lower quartile, median, and upper quartile of the energy proportions of physiological data from multiple scenarios within the classification scene. The above technical solution quantifies signal quality using statistical data, enabling the quantified data to accurately reflect the characteristics of the original data.

[0014] In one possible implementation, calculating the quality assessment parameters of sample data for multiple classification scenarios based on the energy proportion of physiological data corresponding to different scenarios includes: classifying different scenarios to determine multiple classification scenarios; obtaining the energy proportion of multiple physiological data for each classification scenario corresponding to at least two assessment dimensions based on the analysis target; and calculating the lower quartile, median, and upper quartile of the energy proportion of physiological data for each classification scenario corresponding to at least two assessment dimensions. This technical solution assesses signal quality based on quality assessment parameters for different analysis targets, different assessment dimensions, and different classification scenarios, thereby improving assessment accuracy.

[0015] In one possible implementation, evaluating the quality of the physiological detection signals acquired by the detection device based on quality assessment parameters includes: determining whether the quality of the physiological detection signals acquired by the detection device has improved or decreased based on the percentage increase or decrease of the quality assessment parameters for the classification scenarios corresponding to the first assessment dimension relative to the quality assessment parameters for the classification scenarios corresponding to the second assessment dimension. The above technical solution can intuitively reflect the improvement or decrease in quality through the percentage increase or decrease.

[0016] In one possible implementation, determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the increase or decrease ratio of the quality assessment parameters of the classification scenario corresponding to the first assessment dimension relative to the quality assessment parameters of the classification scenario corresponding to the second assessment dimension includes: calculating the lower quartile increase ratio, median increase ratio, and upper quartile increase ratio of the energy proportions of multiple physiological data in the classification scenario corresponding to the first assessment dimension relative to the energy proportions of multiple physiological data in the classification scenario corresponding to the second assessment dimension; and determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile increase ratio, median increase ratio, and upper quartile increase ratio. The above technical solution can intuitively reflect the improvement or decrease in quality through the increase or decrease ratio of statistical data.

[0017] In one possible implementation, determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio includes: determining whether the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio are greater than 0; if the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio are determined to be greater than 0, it is determined that the quality of the physiological detection signal acquired by the detection device under the first evaluation dimension has improved relative to the second evaluation dimension; if the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio are all equal to 0, it is determined that the quality of the physiological detection signal acquired by the detection device under the first evaluation dimension has not changed relative to the second evaluation dimension; if any one of the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio is less than 0, it is determined that the quality of the physiological detection signal acquired by the detection device under the first evaluation dimension has decreased relative to the second evaluation dimension.

[0018] In one possible implementation, determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio includes: calculating the average of the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio; determining whether the average value is greater than 0; if the average value is greater than 0, determining that the quality of the physiological detection signal acquired by the detection device in the first evaluation dimension has improved relative to the second evaluation dimension; if the average value is equal to 0, determining that the quality of the physiological detection signal acquired by the detection device in the first evaluation dimension has not changed relative to the second evaluation dimension; if the average value is less than 0, determining that the quality of the physiological detection signal acquired by the detection device in the first evaluation dimension has decreased relative to the second evaluation dimension. This technical solution can accurately determine whether the quality of the physiological detection signal has improved.

[0019] In one possible implementation, determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile, median, and upper quartile elevation ratios includes: setting weight values ​​for the lower quartile, median, and upper quartile elevation ratios respectively; calculating the sum of the lower quartile, median, and upper quartile elevation ratios based on their weight values; determining whether the sum of the lower quartile, median, and upper quartile elevation ratios is greater than 0; and if the lower quartile is determined... If the sum of the improvement ratios of the lower quartile, median, and upper quartile is greater than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is improved compared to the second evaluation dimension. If the sum of the lower quartile, median, and upper quartile improvement ratios is equal to 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is unchanged compared to the second evaluation dimension. If the sum of the lower quartile, median, and upper quartile improvement ratios is less than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is decreased compared to the second evaluation dimension. This technical solution, by setting weights, can increase the adjustment space of the signal quality evaluation method and reflect the importance of different quality evaluation parameters.

[0020] In one possible implementation, the method further includes upsampling the PPG data in the sample data. The above technical solution improves the accuracy of signal quality assessment by increasing the resolution of the sample data.

[0021] In one possible implementation, the method further includes filtering out low-frequency and / or high-frequency noise from the PPG data in the sample data. This technical solution can eliminate the interference of low-frequency and / or high-frequency noise on signal quality assessment, thereby improving the accuracy of signal quality assessment.

[0022] In one possible implementation, filtering low-frequency and / or high-frequency noise from PPG data in sample data includes: setting an upper and lower threshold for the bandpass frequency using an f-order bandpass filter; inputting the PPG data into the bandpass filter; and filtering out low-frequency and / or high-frequency noise from the PPG data based on the upper and lower thresholds. This technical solution can accurately filter out low-frequency and / or high-frequency noise.

[0023] Secondly, this application provides an electronic device, which includes a memory and a processor:

[0024] Among them, the memory is used to store program instructions;

[0025] The processor is used to read and execute program instructions stored in memory. When the program instructions are executed by the processor, the electronic device performs the above-mentioned physiological detection signal quality assessment method.

[0026] Thirdly, this application provides a computer storage medium storing program instructions that, when executed on an electronic device, cause the electronic device to perform the above-mentioned physical detection signal quality assessment method.

[0027] Furthermore, the technical effects brought about by the second and third aspects can be found in the descriptions of the methods in the above-mentioned method section, and will not be repeated here.

[0028] The physiological detection signal quality assessment method, electronic device, and storage medium provided in this application can quantify the quality of physiological detection signals. Based on the quantified data, the quality of physiological detection signals can be easily assessed, and it can also adapt well to different usage scenarios, thereby facilitating the evaluation of hardware performance in multiple scenarios. Attached Figure Description

[0029] Figure 1A This is a schematic diagram of a user wearing a detection device in a sports scenario, provided in one embodiment of this application.

[0030] Figure 1B This is a schematic diagram of a user wearing a detection device in a static scene, provided in one embodiment of this application.

[0031] Figure 1C This is a schematic diagram of a user wearing a detection device in a sleep scenario, provided in one embodiment of this application.

[0032] Figure 2 This is a schematic diagram of the communication architecture of an electronic device provided in an embodiment of this application.

[0033] Figure 3 This is a flowchart of a physiological detection signal quality assessment method provided in an embodiment of this application.

[0034] Figure 4 This is a flowchart illustrating the acquisition of sample datasets from different scenarios using a detection device, as provided in one embodiment of this application.

[0035] Figure 5 This is a flowchart illustrating the calculation of the energy proportion of physiological data in sample data according to an embodiment of this application.

[0036] Figure 6A This is a schematic diagram of the power spectral density curve before interpolation processing provided in an embodiment of this application.

[0037] Figure 6B This is a schematic diagram of the power spectral density curve after interpolation processing provided in an embodiment of this application.

[0038] Figure 7 This is a schematic diagram of the power spectral density curve provided in an embodiment of this application.

[0039] Figure 8 This is a flowchart of deleting abnormal data provided in an embodiment of this application.

[0040] Figure 9 It is a box plot showing the energy proportions of multiple physiological data in sample data from different scenarios.

[0041] Figure 10 This is a flowchart illustrating the calculation of quality assessment parameters provided in one embodiment of this application.

[0042] Figure 11 This is a flowchart of a physiological detection signal quality assessment method provided in another embodiment of this application.

[0043] Figure 12 This is a flowchart of a physiological detection signal quality assessment method provided in another embodiment of this application.

[0044] Figure 13 This is a flowchart of a physiological detection signal quality assessment method provided in another embodiment of this application.

[0045] Figure 14 This is an architectural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0046] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this 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 this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or". For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. "At least one" refers to one or more. "More than one" refers to two or more. For example, at least one of a, b, or c can represent: a, b, c, a and b, a and c, b and c, and a, b, and c (seven cases).

[0048] See Figures 1A-1C The diagram illustrates the application of the detection device in different scenarios. Taking actual user experience as an example, users are in various states throughout the day, including outdoor exercise, indoor sitting, and sleep. In these different states, users can use the detection device to measure physiological parameters, with photoelectric pulse gaussogram (PPG) being the primary detection method. PPG is a non-invasive detection method that uses photoelectric means to detect changes in vascular volume in living tissue. The raw PPG signal includes various physiological information such as heart rate, blood oxygen saturation, and blood pressure. Due to its small size, portability, and ease of use, PPG measurement devices are widely used for all-weather monitoring of various physiological indicators. However, this all-weather monitoring is often accompanied by various complex usage scenarios. These complex scenarios mean that the PPG signal must withstand complex and varied noise interference. Since the anti-interference capability of the hardware device is strongly correlated with the effectiveness of the algorithm developed based on the PPG signal generated by the device, the signal quality in multiple scenarios cannot be accurately quantified in a standardized manner. This makes it difficult to reasonably evaluate the hardware performance in multiple scenarios and to optimize the hardware for specific scenarios.

[0049] In addition, the evaluation metrics currently used by hardware developers are applicable to a relatively limited range of scenarios. Although many hardware manufacturers have relevant evaluation schemes, these schemes are not refined enough, and the evaluation objects of the metrics are single samples rather than multi-scenario sample sets. Furthermore, they lack the ability to identify and correct deviations caused by the gold standard.

[0050] To address the technical problem of the inability to effectively evaluate the PPG signal quality of the aforementioned detection devices, this application provides a method for evaluating the quality of physiological detection signals. The method is applied in an electronic device 100.

[0051] See Figure 2The diagram shown is a schematic representation of the communication architecture of an electronic device provided in an embodiment of this application. The electronic device 100 and the detection device 200 are connected via a network 300. For example, the network 300 can be at least one of a 2G network, a 3G network, a 4G-LTE network, or a 5G New Radio network. It should be noted that the above description does not constitute a limitation on the communication architecture of the electronic device 100 in the embodiments of this application, and the communication architecture diagram of the electronic device 100 in the embodiments of this application is not limited to [the specific example shown]. Figure 2 Example provided. The detection device 200 can be a device used for physiological detection, such as a heart rate monitor or pulse meter, or a device with physiological detection functions, such as an electronic bracelet or smartwatch.

[0052] See Figure 3 The diagram shown is a flowchart of a physiological detection signal quality assessment method provided in an embodiment of this application. The method is applied in an electronic device 100 and specifically includes:

[0053] S301, Obtain sample datasets of the detection equipment in different scenarios.

[0054] In one embodiment of this application, the acquisition of sample data from the detection device in different scenarios can be referred to Figure 4 The detailed process shown includes: S3011, setting multiple preset scenarios based on tags. The tags can be set according to requirements and may include, but are not limited to, user status, user environment, and movement type. For example, if the tag is "all," the multiple preset scenarios can include all scenarios where the detection device might be used. If the tag is "indoor," the multiple preset scenarios can include indoor movement, indoor stillness, etc. If the tag is "outdoor," the multiple preset scenarios can include outdoor movement, outdoor stillness, etc. If the tag is "still," the multiple preset scenarios can include indoor stillness, outdoor stillness, etc. If the tag is "movement," the multiple preset scenarios can include indoor movement, outdoor movement, etc. If the tag is "movement type," the multiple preset scenarios can include cycling, walking, running, etc. If the tag is "status," the preset scenarios can include stillness, movement, etc.

[0055] In one embodiment of this application, preset scenarios can be prepared using simulation devices, such as robots, with the detection device mounted on the robot to achieve multiple preset scenarios. In other embodiments, preset scenarios can also be prepared using actual users and environments, with the detection device worn by the user and placed in different environments to achieve multiple preset scenarios.

[0056] S3012, Connect the detection device to the data acquisition terminal. Specifically, connect the accessories of the detection device to the data acquisition terminal respectively. For example, the detection device is an electronic bracelet with heart rate acquisition function, which includes a bracelet and a heart rate belt. The bracelet is worn on the user's wrist and can collect the user's PPG signal in real time through sensors, while the heart rate belt is worn near the heart and can collect the user's electrocardiogram signal in real time through sensors.

[0057] In one embodiment of this application, the data acquisition terminal is an application installed and running on the detection device. This application is used to collect data acquired by the sensors. The detection device can send the PPG and ECG signals acquired by the sensors to the application in real time, and transmit the PPG and ECG signals to electronic devices via a network.

[0058] In other embodiments, the data acquisition end is an application installed and running on the electronic device, which is used to collect data collected by the detection device. The detection device can transmit PPG and ECG signals collected by the sensors to the electronic device in real time via a network, and the electronic device stores the received PPG and ECG signals in the application.

[0059] S3013, Data acquisition is performed based on a single scenario to generate a single sample data. Specifically, in any of multiple scenarios, the user performs an action based on the scenario. For example, if the scenario is outdoor exercise, the user exercises outdoors. During the user's action, the detection device starts working, acquiring a preset number of PPG signals and ECG signals through sensors, and stops acquiring data after the sample data acquisition volume reaches the target. Optionally, the preset number is fifty, that is, fifty PPG signals and fifty ECG signals are acquired respectively.

[0060] S3014, Output a multi-scenario sample dataset based on sample data from each scenario. In one embodiment of this application, sample data from each scenario are aggregated to output a multi-scenario sample dataset.

[0061] S302, Select sample data for a scene from the sample dataset and divide the sample data into physiological data and PPG data.

[0062] In one embodiment of this application, selecting sample data for a scene from a sample dataset includes: arbitrarily selecting sample data for a scene from the output multi-scene sample dataset.

[0063] In one embodiment of this application, dividing sample data into physiological data and PPG data includes: converting the PPG signal in the sample data into physiological data H, and converting the PPG signal into single-channel PPG data P0. Preferably, the physiological data H is heart rate tag data, and the PPG data is detected and acquired by the PPG sensor of the detection device. The PPG sensor converts the emitted light signal and the light signal reflected by human tissue (muscle, bone, vein, etc.) into electrical signals to obtain PPG data. The electrical signal can be a voltage signal, i.e., the PPG data is voltage. In other embodiments, the physiological data H can also be pulse data.

[0064] S303, calculate the energy percentage of physiological data in the sample data based on physiological data H and PPG data P0.

[0065] In one embodiment of this application, multiple energy percentages of frequencies corresponding to multiple physiological data are calculated based on the physiological data H and the PPG data P0, and the average value of the multiple energy percentages of frequencies corresponding to the multiple physiological data is calculated as the energy percentage of the physiological data.

[0066] Specifically, see Figure 5 The detailed process shown, in one embodiment of this application, includes calculating multiple energy percentages of frequencies corresponding to multiple physiological data based on the physiological data H and the PPG data P0, including: S3031, performing a short-time Fourier transform on the PPG data P0 to obtain the power spectral density data of the PPG data P0. Since a preset number of PPG signals have been collected, a preset number of PPG signals can be generated. The physiological data is the heart rate per second. The expression for the short-time Fourier transform is:

[0067] .

[0068] Where z(u) is the original PPG signal, and g(ut) is the window function. The short-time Fourier transform uses a Hamming window with a window length of 2t (where t is an integer) seconds of data length and a step size of 1 second of data length. Assume the preset number of PPG data points is... The PPG data P0 is subjected to this short-time Fourier transform to obtain the power spectral density data. This serves as the frequency domain characteristic of the PPG data P0.

[0069] S3032 performs interpolation processing on the power spectral density data.

[0070] See Figure 6AThe image shows the original power spectral density curve. In one embodiment of this application, the power spectral density data is interpolated using a frequency domain filling method. Specifically, the discrete power spectral density data is fitted to a quadratic spline curve, and interpolation is performed based on the quadratic spline curve by a first preset factor. See also... Figure 6B The figure shows the power spectral density curve after interpolation. The expression for the quadratic spline curve is:

[0071] .

[0072] Where A1, A2, and A3 are coefficients in two-dimensional vector form. Optionally, the first preset multiplier is 50. Interpolation can expand the power spectral density data to 50 times the original, thereby improving the accuracy of subsequent analysis of PPG signal quality. For example, if the initial power spectral density data contains 100 data points, the interpolated power spectral density data will contain 5000 data points.

[0073] In another embodiment of this application, the power spectral density data can also be interpolated based on time-domain zero-padding. Specifically, before performing a short-time Fourier transform on the PPG data, the PPG data is interpolated by a first preset factor using time-domain zero-padding. Then, a short-time Fourier transform is performed on the interpolated PPG data to obtain power spectral density data that is 50 times greater than the original data. For example, if the initial PPG signal contains 100 data points, 4900 zeros are padded into the PPG signal.

[0074] S3033, determine the time point z corresponding to the center position of multiple Hamming windows, and obtain the physiological data (e.g., heart rate value) corresponding to each time point z.

[0075] For example, if the time point corresponding to the center position of the Hamming window is the 5th second, then the heart rate value at the 5th second can be obtained from the physiological data H. Multiple heart rate values ​​can be obtained through multiple Hamming windows.

[0076] S3034 converts the acquired physiological data into a frequency range.

[0077] See Figure 7The figure shows the interpolated power spectral density curve, which is generated based on power spectral density data. The horizontal axis represents frequency, and the vertical axis represents energy. In one embodiment of this application, the physiological data corresponding to time point z is the heart rate value per minute, for example, 80 beats / min (BPM). The heart rate value is converted to a heart rate per second, i.e., 1.33 beats / second, thus corresponding to the frequency on the horizontal axis of the power spectral density curve. Then, the corresponding frequency range is determined according to a preset range of physiological data. Optionally, taking heart rate as an example, the preset range is ±5 BPM. For example, if the heart rate value is 80 BPM, then the heart rate value range is 75-85 BPM, and the frequency range corresponding to the heart rate value range is 1.25-1.42 beats / second (Hz).

[0078] S3035 calculates the energy percentage corresponding to the frequency range based on power spectral density data.

[0079] In one embodiment of this application, the formula for calculating the energy percentage (IER) corresponding to the frequency range is:

[0080] .

[0081] Here, signal power is the energy value within the specified frequency range, and noise power is the energy value outside this range. Based on the example above, the vertical axis value corresponding to the frequency points (i.e., each frequency value) in the power spectral density curve between 1.25 and 1.42 Hz is the energy value. Signal power is the sum of the energy values ​​at the corresponding frequency points within the 1.25-1.42 Hz range, and noise power is the sum of the energy values ​​at all other frequency points.

[0082] S3036, outputs a list of energy percentages for multiple physiological data in the sample data based on the energy percentages corresponding to multiple frequency ranges.

[0083] In one embodiment of this application, the energy percentage of each physiological data point is summarized to form an energy percentage list, and the energy percentage list is output. For example, the energy percentage list is... .

[0084] In one embodiment of this application, the formula for calculating the average value of multiple energy percentages corresponding to the multiple physiological data frequencies is as follows:

[0085] .

[0086] That is, the energy percentage R of physiological data in the sample data is obtained by calculating the average of all energy percentages in the energy percentage list.

[0087] In one embodiment of this application, if the energy ratio R of physiological data in the sample data corresponding to the current scene is calculated, the energy ratio R of physiological data in a single sample data corresponding to another scene is calculated using the above method, until the energy ratio R of physiological data in all single sample data is calculated.

[0088] In another embodiment of this application, calculating the energy percentages of multiple frequencies corresponding to multiple physiological data based on the physiological data H and the PPG data P0 further includes: deleting abnormal data from the energy percentage list E of multiple physiological data in the sample data.

[0089] Specifically, see Figure 8 The detailed process shown, in one embodiment of this application, includes deleting abnormal data from the energy percentage list E of multiple physiological data in the sample data, which includes: S801, calculating the upper quartile Q1 in the energy percentage list E. E and lower quartile Q3 E Among them, the upper quartile Q1 E The position = (n+1)*0.75, the lower quartile Q3 E The position is (n+1)*0.25, where n is the number of frames in the energy percentage list E (the total number of energy percentage data).

[0090] S802, calculates the upper and lower thresholds for outlier data.

[0091] In one embodiment of this application, the upper limit threshold for abnormal data The lower limit threshold for abnormal data The values ​​of a and b can be preset and adjusted; optionally, a is 2.5 and b is 1.5.

[0092] See Figure 9 The figure shows a box plot representing the energy percentage of physiological data in sample data from different scenarios. The box plot is based on the upper quartile Q1 in the energy percentage list E. E Q3 (lower quartile) E The upper limit threshold U1 and the lower limit threshold U2 are generated.

[0093] S803, determine that energy percentage data that is less than or equal to the lower threshold U2 or greater than or equal to the upper threshold U1 is abnormal data.

[0094] like Figure 9 As shown, the top line of each box plot corresponds to the upper limit threshold U1, the bottom line corresponds to the lower limit threshold U2, and the lines inside the rectangle correspond to the median of multiple energy percentages. Energy percentage data that are less than or equal to the lower limit threshold U2 or greater than or equal to the upper limit threshold U1 are outside the energy percentage box plot and are therefore considered outliers.

[0095] S804, Output the energy percentage list after deleting abnormal data. p .

[0096] In one embodiment of this application, after deleting abnormal data from the energy percentage list E, the energy percentage list E is obtained. p Output the energy percentage list E p .

[0097] In another embodiment of this application, the formula for calculating the average value of multiple energy percentages corresponding to the multiple physiological data frequencies is as follows:

[0098] .

[0099] S304, calculate the quality assessment parameters of sample data for multiple classification scenarios based on the energy proportion of physiological data in different scenarios.

[0100] In one embodiment of this application, the quality assessment parameters include, but are not limited to, the lower quartile Q1, median Q2, and upper quartile Q3 of the energy percentage. Specifically, the position of the lower quartile Q1 is (n+1)*0.25, the position of the median Q2 is (n+1)*0.5, and the position of the upper quartile Q3 is (n+1)*0.75.

[0101] Specifically, see Figure 10 The detailed process shown calculates quality assessment parameters for sample data of multiple classification scenarios based on the energy proportion of physiological data in different scenarios, including: S3041, classifying different scenarios to determine multiple classification scenarios. In one embodiment of this application, multiple different scenarios can be classified according to labels, and multiple scenarios with the same label can be classified into one classification scenario. For example, if the label is indoor, indoor sports, indoor stillness, etc., can be classified as indoor scenarios. If the label is outdoor, outdoor sports, outdoor stillness, etc., can be classified as outdoor scenarios. If the label is stillness, it can include indoor stillness, outdoor stillness, etc. If the label is sports, multiple preset scenarios can include indoor sports, outdoor sports, etc. If the label is cycling, cycling scenarios of different users can be classified into cycling scenarios.

[0102] S3042, based on the analysis target, obtain the energy proportion of multiple physiological data for classification scenarios corresponding to at least two evaluation dimensions.

[0103] In one embodiment of this application, the analysis target can be set from different dimensions such as device dimension, scenario dimension, or user dimension, depending on the requirements. For example, the analysis target corresponding to the device dimension can be to evaluate the PPG signal quality collected by detection devices of the same type but different versions, or to evaluate the PPG signal quality collected by detection devices of different types. The analysis target corresponding to the scenario dimension can be to evaluate the PPG signal quality of different classification scenarios. The analysis target corresponding to the user dimension can be to evaluate the PPG signal quality collected by the same detection device when worn on different parts of the user's body in the same classification scenario, or to evaluate the PPG signal quality collected by the same detection device when detecting users of different age groups in the same classification scenario.

[0104] In one embodiment of this application, the evaluation dimensions are determined based on the analytical target and are at least two reference objects under an analytical target. For example, the at least two evaluation dimensions may include the previous generation of testing equipment and the new generation of testing equipment, may include electronic bracelets and smartwatches, or may include the 20-30 age group and the 30-40 age group.

[0105] After determining the evaluation dimensions, the energy proportions of multiple physiological data in the classification scenarios are obtained. For example, the first evaluation dimension is the previous generation detection device, the second evaluation dimension is the new generation detection device, and the classification scenario is a static scenario. The energy proportions of multiple physiological data of the previous generation detection device in static scenarios (e.g., indoor static, outdoor static) and the energy proportions of multiple physiological data of the new generation detection device in static scenarios (e.g., indoor static, outdoor static) are obtained.

[0106] S3043, calculate the lower quartile, median and upper quartile of the physiological data energy proportions for the classification scenarios corresponding to at least two evaluation dimensions.

[0107] Based on the above examples, calculate the lower quartile, median, and upper quartile of the energy proportion of multiple physiological data in a static scene for the previous generation detection equipment, and the lower quartile, median, and upper quartile of the energy proportion of multiple physiological data in a static scene for the new generation detection equipment.

[0108] S305 evaluates the quality of the PPG signal acquired by the testing equipment based on quality assessment parameters.

[0109] In one embodiment of this application, the quality of the PPG signal collected by the detection device is determined based on the increase / decrease ratio of the quality assessment parameters of the classification scene corresponding to the first assessment dimension relative to the quality assessment parameters of the classification scene corresponding to the second assessment dimension.

[0110] Specifically, if the first increase ratio of the lower quartile of the energy proportion of multiple physiological data in the first evaluation dimension under the classification scenario relative to the lower quartile of the energy proportion of multiple physiological data in the second evaluation dimension under the classification scenario, the second increase ratio of the median of the energy proportion of multiple physiological data in the first evaluation dimension under the classification scenario relative to the median of the energy proportion of multiple physiological data in the second evaluation dimension under the classification scenario, and the third increase ratio of the upper quartile of the energy proportion of multiple physiological data in the first evaluation dimension under the classification scenario relative to the upper quartile of the energy proportion of multiple physiological data in the second evaluation dimension under the classification scenario are all greater than 0, then the PPG signal quality under the first evaluation dimension is determined to be improved compared to the second evaluation dimension. If the first, second, and third increase ratios are all equal to 0, then the PPG signal quality under the first evaluation dimension is determined to be unchanged compared to the second evaluation dimension. If any one of the first, second, and third increase ratios is less than 0, then the PPG signal quality under the first evaluation dimension is determined to be decreased compared to the second evaluation dimension.

[0111] For example, the lower quartile of the energy percentage of the new generation of detection equipment in a static scenario is Q1. new The median is Q2 new The upper quartile is Q3 new The lower quartile of the energy percentage of the previous generation detection equipment in a static scenario is Q1. old The median is Q2 old The upper quartile is Q3 old The first lift ratio Q1 of the lower quartile is calculated based on the two lower quartiles. increase The second increase in the median, Q2, is calculated based on the two medians. increase And the third increase ratio Q3 of the upper quartiles calculated based on the two upper quartiles. increase The calculation formulas are as follows:

[0112] ;

[0113] ;

[0114] .

[0115] Determine the first lift ratio Q1 of the calculated lower quartile. increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase Is it greater than 0? If the first increase ratio Q1 of the lower quartile is determined... increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increaseAll values ​​are greater than 0, confirming that the quality of the PPG signal acquired by the new generation detection equipment is improved compared to the previous generation. If we determine the first improvement ratio Q1 of the lower quartile... increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase If any one of the proportions is less than 0, it indicates that the quality of the PPG signal acquired by the new generation detection equipment is lower than that of the previous generation detection equipment. If the first improvement proportion Q1 of the lower quartile is determined... increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase If all values ​​are equal to 0, it indicates that the quality of the PPG signal acquired by the new generation of detection equipment is unchanged compared to the previous generation of detection equipment.

[0116] Taking different products as examples, the lower quartile of the energy percentage of smartwatches in static scenarios is Q1. watch The median is Q2 watch The upper quartile is Q3 watch The lower quartile of the energy percentage of the electronic bracelet in a static scenario is Q1. band The median is Q2 band The upper quartile is Q3 band Calculate the first quartile improvement (Q1) of the smartwatch relative to the electronic bracelet. increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase The calculation formulas are as follows:

[0117] ;

[0118] ;

[0119] .

[0120] Determine the first lift ratio Q1 of the calculated lower quartile. increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase Are all values ​​greater than 0? If the first increase in the lower quartile is determined, Q1 increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase All values ​​are greater than 0, confirming that the quality of the PPG signal collected by the smartwatch is improved compared to that of the electronic bracelet. If we determine the first improvement ratio Q1 of the lower quartile... increase The second increase in median in Q2 increaseand the third increase in the upper quartile Q3 increase If any one of the proportions is less than 0, it indicates that the quality of the PPG signal collected by the smartwatch is lower than that of the electronic bracelet. If the first improvement proportion Q1 of the lower quartile is determined... increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase If all values ​​are equal to 0, it indicates that the quality of the PPG signal collected by the smartwatch is unchanged compared to that of the electronic bracelet.

[0121] In another embodiment of this application, the average value of the improvement ratio of the quality assessment parameter can be calculated, that is, the first improvement ratio Q1 of the lower quartile can be calculated. increase The second increase in median in Q2 increase and the third increase in the upper quartile Q3 increase The average value. If the average improvement rate of the quality assessment parameters is greater than 0, it indicates that the PPG signal quality under the first assessment dimension is improved relative to the second assessment dimension.

[0122] In another embodiment of this application, weight values ​​can be set for the improvement ratios of each quality assessment parameter, and the sum of the improvement ratios of each quality assessment parameter can be calculated based on the weight values. If the sum of the improvement ratios of each quality assessment parameter is greater than 0, it indicates that the PPG signal quality under the first assessment dimension is improved relative to the second assessment dimension. Wherein, the first improvement ratio Q1 of the lower quartile... increase The weight is e, and the second increase ratio of the median is Q2. increase The weights are f and the third increase ratio of the upper quartile Q3. increase If the weight is g, then the sum of the improvement ratios of each quality assessment parameter is d = e * Q1. increase +f*Q2 increase +g*Q3 increase .

[0123] See Figure 11 The diagram shown is a flowchart of a physiological detection signal quality assessment method provided in an embodiment of this application. The method is applied in an electronic device 100 and specifically includes:

[0124] S101-S102 are the same as S301-S302 above, and will not be repeated here.

[0125] S103, upsample the PPG data P0 in the sample data.

[0126] In one embodiment of this application, upsampling the PPG data P0 in the sample data includes: upsampling the PPG data P0 in the sample data by a second preset factor. Optionally, the second preset factor is five times.

[0127] Specifically, assume that the PPG data P0 in the sample data includes n+1 data nodes. The PPG data P0 is upsampled by a quadratic interpolation of five times, and the upsampled PPG data is output, thus obtaining the upsampled PPG data P1. That is, a linear equation in two variables can be used as an interpolation function to interpolate the PPG data P0.

[0128] S104, calculate the energy percentage of physiological data in the sample data based on physiological data H and PPG data P1.

[0129] S105-S106 are the same as S304-S305 above, and will not be repeated here.

[0130] See Figure 12 The diagram shown is a flowchart of a physiological detection signal quality assessment method provided in another embodiment of this application. The method is applied in an electronic device 100 and specifically includes:

[0131] S201-S203 are the same as S101-S103 above, and will not be repeated here.

[0132] S204 filters out low-frequency / high-frequency noise in PPG data P1.

[0133] In one embodiment of this application, filtering low-frequency / high-frequency noise from PPG data P1 includes: inputting PPG data P1 into an f-order bandpass filter, and outputting filtered PPG data P2 through the f-order bandpass filter. The upper threshold of the f-order bandpass filter is F. h The lower threshold is F l The upper threshold of the f-th order bandpass filter is F. h To filter out high-frequency noise in PPG data P1, the lower threshold of the f-order bandpass filter is F. l To filter out low-frequency noise in PPG data P1. This includes the order f and the upper threshold F. h and lower limit threshold F l It can be configured according to requirements; for example, an f-order bandpass filter can be a fourth-order bandpass filter with an upper threshold of F. h It can be 400Hz, with a lower threshold F. l It is 100Hz.

[0134] S205, calculate the energy percentage of physiological data in the sample data based on physiological data H and PPG data P2.

[0135] S206-S207 are the same as S105-S106 above, and will not be repeated here.

[0136] See Figure 13 The diagram shown is a flowchart of a physiological detection signal quality assessment method provided in another embodiment of this application. The method is applied in an electronic device 100 and specifically includes:

[0137] S1301 collects multi-scenario sample sets.

[0138] S1302, Select a single sample.

[0139] S1303, acquire physiological data from a single sample.

[0140] S1304, Obtain PPG data for a single sample.

[0141] S1305 upsamples the PPG data.

[0142] S1306 filters out low-frequency and high-frequency noise in PPG data.

[0143] S1307, the percentage of a single sample whose heart rate is ±5 BPM is calculated.

[0144] S1308, Abnormal frame deletion.

[0145] S1309, the percentage of a single sample whose heart rate is within ±5 BPM is calculated.

[0146] S1310, determine if there are any remaining unanalyzed samples. If yes, return to S1302. If no, proceed to S1311.

[0147] S1311, Statistical analysis of all sample results.

[0148] S1312, Hardware Iteration Effect Evaluation, evaluates the quality of PPG signals acquired by different generations of hardware.

[0149] The physiological signal quality assessment method provided in this application can automatically determine the iterative baseline based on statistical analysis of multi-scenario sample groups. It uses a consistent target (e.g., gold standard heart rate) to unify hardware and algorithm indicators at the hardware evaluation level, promoting standardized iteration of hardware signal quality. The method also increases frequency domain resolution and thus improves the accuracy of energy percentage calculation through frequency domain interpolation. Furthermore, the method can detect abnormal evaluation points in single samples, correct gold standard bias, and increase sample validity.

[0150] See Figure 14As shown, the electronic device 100 can be a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, as well as cellular phone, personal digital assistant (PDA), augmented reality (AR) device, virtual reality (VR) device, artificial intelligence (AI) device, wearable device, in-vehicle device, smart home device and / or smart city device. The embodiments of this application do not impose any special restrictions on the specific type of the electronic device 100.

[0151] Electronic device 100 may include processor 110, external memory interface 120, internal memory 121, Universal Serial Bus (USB) interface 130, charging management module 140, power management module 141, battery 142, antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber Identification Module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an accelerometer sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0152] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0153] Processor 110 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0154] The controller can generate operation control signals based on the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0155] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0156] In some embodiments, the processor 110 may include one or more interfaces. Interfaces may include an Inter-integrated Circuit (I2C) interface, an Inter-integrated Circuit Sound (I2S) interface, a Pulse Code Modulation (PCM) interface, a Universal Asynchronous Receiver / Transmitter (UART) interface, a Mobile Industry Processor Interface (MIPI) interface, a General-Purpose Input / Output (GPIO) interface, a Subscriber Identity Module (SIM) interface, and / or a Universal Serial Bus (USB) interface, etc.

[0157] The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple I2C buses. The processor 110 can couple to the touch sensor 180K, charger, flash, camera 193, etc., through different I2C bus interfaces. For example, the processor 110 can couple to the touch sensor 180K through the I2C interface, enabling the processor 110 and the touch sensor 180K to communicate through the I2C bus interface, thereby realizing the touch function of the electronic device 100.

[0158] The I2S interface can be used for audio communication. In some embodiments, the processor 110 may include multiple I2S buses. The processor 110 can be coupled to the audio module 170 via the I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 via the I2S interface to enable the function of answering phone calls through a Bluetooth headset.

[0159] The PCM interface can also be used for audio communication, sampling, quantizing, and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 can be coupled via the PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 via the PCM interface, enabling the function of answering phone calls through a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

[0160] The UART interface is a universal serial data bus used for asynchronous communication. This bus can be a bidirectional communication bus. It converts the data to be transmitted between serial and parallel communication. In some embodiments, the UART interface is typically used to connect the processor 110 and the wireless communication module 160. For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 via the UART interface to implement Bluetooth functionality. In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 via the UART interface to enable music playback through Bluetooth headphones.

[0161] The MIPI interface can be used to connect the processor 110 to peripheral devices such as the display screen 194 and the camera 193. The MIPI interface includes a Camera Serial Interface (CSI) and a Display Serial Interface (DSI). In some embodiments, the processor 110 and the camera 193 communicate via the CSI interface to enable the electronic device 100 to capture images. The processor 110 and the display screen 194 communicate via the DSI interface to enable the electronic device 100 to display images.

[0162] The GPIO interface can be configured via software. It can be configured as a control signal or a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 to a camera 193, a display screen 194, a wireless communication module 160, an audio module 170, a sensor module 180, etc. The GPIO interface can also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, etc.

[0163] USB port 130 is a USB standard compliant interface, specifically a Mini USB port, Micro USB port, USB Type-C port, etc. USB port 130 can be used to connect a charger to charge electronic device 100, and can also be used for data transfer between electronic device 100 and peripheral devices. It can also be used to connect headphones for audio playback. This interface can also be used to connect other electronic devices 100, such as AR devices.

[0164] It is understood that the interface connection relationships between the modules illustrated in the embodiments of the present invention are merely illustrative and do not constitute a structural limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0165] The charging management module 140 receives charging input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 receives charging input from the wired charger via a USB interface 130. In some wireless charging embodiments, the charging management module 140 receives wireless charging input via the wireless charging coil of the electronic device 100. While charging the battery 142, the charging management module 140 can also supply power to the electronic device 100 via the power management module 141.

[0166] The power management module 141 connects the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, providing power to the processor 110, internal memory 121, display screen 194, camera 193, and wireless communication module 160, etc. The power management module 141 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some other embodiments, the power management module 141 may also be located within the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may be located in the same device.

[0167] The wireless communication function of electronic device 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor, etc.

[0168] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with a tuning switch.

[0169] The mobile communication module 150 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low-noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 150 may be housed in the processor 110. In some embodiments, at least some functional modules of the mobile communication module 150 and at least some modules of the processor 110 may be housed in the same device.

[0170] The modem processor may include a modulator and a demodulator. The modulator modulates the low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs sound signals through an audio device (not limited to speaker 170A, receiver 170B, etc.) or displays images or videos through the display screen 194. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 110 and may be housed in the same device as the mobile communication module 150 or other functional modules.

[0171] The wireless communication module 160 can provide solutions for wireless communication applications on the electronic device 100, including Wireless Local Area Networks (WLANs) (such as Wireless Fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), and Infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0172] In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150, and antenna 2 is coupled to wireless communication module 160, enabling electronic device 100 to communicate with networks and other devices via 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 technologies, etc. The GNSS may include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the Beidou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0173] Electronic device 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. Processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0174] Display screen 194 is used to display images, videos, etc. Display screen 194 includes a display panel. The display panel may be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a minimized display, a micro-OLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, electronic device 100 may include one or N displays 194, where N is a positive integer greater than 1.

[0175] Electronic device 100 can perform shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

[0176] The ISP (Image Signal Processor) is used to process data fed back from the camera 193. For example, when taking a picture, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, and the camera's photosensitive element transmits the electrical signal to the ISP for processing, transforming it into an image visible to the naked eye. The ISP can also perform algorithmic optimization of image noise, brightness, and skin tone. The ISP can also optimize parameters such as exposure and color temperature of the shooting scene. In some embodiments, the ISP can be set in the camera 193.

[0177] Camera 193 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then passed to an ISP for conversion into a digital image signal. The ISP outputs the digital image signal to a DSP for processing. The DSP converts the digital image signal into image signals in standard RGB, YUV, or other formats. In some embodiments, the electronic device 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0178] Digital signal processors (DSPs) are used to process digital signals. Besides digital image signals, they can also process other digital signals. For example, when electronic device 100 selects a frequency, the DSP can perform Fourier transforms on the frequency energy.

[0179] Video codecs are used to compress or decompress digital video. Electronic device 100 may support one or more video codecs. Thus, electronic device 100 can play or record video in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0180] NPU stands for Neural Network (NN) computing processor. By borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it can rapidly process input information and continuously learn on its own. NPUs enable intelligent cognitive applications in electronic devices, such as image recognition, facial recognition, speech recognition, and text understanding.

[0181] Internal memory 121 may include one or more random access memory (RAM) and one or more non-volatile memory (NVM).

[0182] Random access memory can include static random-access memory (SRAM), dynamic random-access memory (DRAM), synchronous dynamic random-access memory (SDRAM), and double data rate synchronous dynamic random-access memory (DDR SDRAM, such as fifth-generation DDR SDRAM, which is generally called DDR5 SDRAM).

[0183] Non-volatile memory can include disk storage devices and flash memory.

[0184] Flash memory can be classified according to its operating principle, including NOR FLASH, NAND FLASH, 3D NAND FLASH, etc.; according to the level of the storage cell, including single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), quad-level cell (QLC), etc.; and according to the storage specification, including universal flash storage (UFS) and embedded multi-media card (eMMC), etc.

[0185] The random access memory can be directly read and written by the processor 110. It can be used to store executable programs (such as machine instructions) of the operating system or other running programs, as well as user and application data.

[0186] Non-volatile memory can also store executable programs and user and application data, and can be pre-loaded into random access memory for direct reading and writing by the processor 110.

[0187] The external memory interface 120 can be used to connect to external non-volatile memory, thereby expanding the storage capacity of the electronic device 100. The external non-volatile memory communicates with the processor 110 through the external memory interface 120 to perform data storage functions. For example, music, video, and other files can be stored in the external non-volatile memory.

[0188] Internal memory 121 or external memory interface 120 is used to store one or more computer programs. The one or more computer programs are configured to be executed by processor 110. The one or more computer programs include multiple instructions, which, when executed by processor 110, can implement the physiological detection signal quality assessment method executed on electronic device 100 in the above embodiments, so as to realize the physiological detection signal quality assessment display function of electronic device 100.

[0189] Electronic device 100 can implement audio functions, such as music playback and recording, through audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, and application processor.

[0190] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 may be located in the processor 110, or some functional modules of the audio module 170 may be located in the processor 110.

[0191] The speaker 170A, also known as a "loudspeaker," is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music or make hands-free calls through the speaker 170A.

[0192] The receiver 170B, also known as the "earpiece," is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a telephone call or voice message, the receiver 170B can be brought close to the ear to listen to the voice.

[0193] Microphone 170C, also known as a "microphone" or "voice transducer," is used to convert sound signals into electrical signals. When making a phone call or sending a voice message, the user can speak by bringing their mouth close to microphone 170C, inputting the sound signal into microphone 170C. Electronic device 100 may have at least one microphone 170C. In some embodiments, electronic device 100 may have two microphones 170C, which, in addition to collecting sound signals, can also perform noise reduction. In other embodiments, electronic device 100 may also have three, four, or more microphones 170C, which can collect sound signals, reduce noise, identify the sound source, and perform directional recording, etc.

[0194] The 170D headphone jack is used to connect wired headphones. The 170D headphone jack can be a USB 130 interface or a 3.5mm Open Mobile Terminal Platform (OMTP) standard interface, a CTIA (Cellular Telecommunications Industry Association of the USA) standard interface.

[0195] Pressure sensor 180A is used to sense pressure signals and convert them into electrical signals. In some embodiments, pressure sensor 180A can be disposed on display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, and capacitive pressure sensors. A capacitive pressure sensor may include at least two parallel plates with conductive material. When force is applied to pressure sensor 180A, the capacitance between the electrodes changes. Electronic device 100 determines the pressure intensity based on the change in capacitance. When a touch operation is applied to display screen 194, electronic device 100 detects the intensity of the touch operation based on pressure sensor 180A. Electronic device 100 can also calculate the touch position based on the detection signal from pressure sensor 180A. In some embodiments, touch operations applied to the same touch position but with different touch operation intensities can correspond to different operation commands. For example, when a touch operation with an intensity less than a first pressure threshold is applied to the SMS application icon, a command to view an SMS is executed. When a touch operation with an intensity greater than or equal to the first pressure threshold is applied to the SMS application icon, a command to create a new SMS is executed.

[0196] The gyroscope sensor 180B can be used to determine the motion attitude of the electronic device 100. In some embodiments, the gyroscope sensor 180B can determine the angular velocity of the electronic device 100 about three axes (i.e., the x, y, and z axes). The gyroscope sensor 180B can be used for image stabilization. For example, when the shutter is pressed, the gyroscope sensor 180B detects the angle of the shake of the electronic device 100, calculates the distance that the lens module needs to compensate based on the angle, and allows the lens to counteract the shake of the electronic device 100 by moving in the opposite direction, thus achieving image stabilization. The gyroscope sensor 180B can also be used in navigation and motion-sensing game scenarios.

[0197] The barometric pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates altitude using the air pressure value measured by the barometric pressure sensor 180C to assist in positioning and navigation.

[0198] The magnetic sensor 180D includes a Hall sensor. The electronic device 100 can use the magnetic sensor 180D to detect the opening and closing of the flip cover. In some embodiments, when the electronic device 100 is a flip phone, the electronic device 100 can detect the opening and closing of the flip cover using the magnetic sensor 180D. Then, based on the detected opening and closing state of the cover or the flip cover, features such as automatic flip unlocking can be set.

[0199] The accelerometer 180E can detect the magnitude of acceleration of electronic device 100 in various directions (typically three axes). When electronic device 100 is stationary, it can detect the magnitude and direction of gravity. It can also be used to identify the posture of electronic device 100, and can be applied to applications such as screen orientation switching and pedometers.

[0200] A distance sensor 180F is used to measure distance. Electronic device 100 can measure distance via infrared or laser. In some embodiments, during a shooting scene, electronic device 100 can utilize the distance sensor 180F to measure distance for rapid focusing.

[0201] The proximity sensor 180G may include, for example, a light-emitting diode (LED) and a light detector, such as a photodiode. The LED may be an infrared LED. The electronic device 100 emits infrared light outward through the LED. The electronic device 100 uses the photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100. When insufficient reflected light is detected, the electronic device 100 can determine that there is no object near the electronic device 100. The electronic device 100 may use the proximity sensor 180G to detect when a user holds the electronic device 100 close to their ear for a call, so as to automatically turn off the screen to save power. The proximity sensor 180G can also be used in holster mode and pocket mode for automatic unlocking and locking of the screen.

[0202] The ambient light sensor 180L is used to sense the brightness of ambient light. The electronic device 100 can adaptively adjust the brightness of the display screen 194 based on the sensed ambient light brightness. The ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also work with the proximity sensor 180G to detect whether the electronic device 100 is in a pocket to prevent accidental touches.

[0203] The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can utilize the characteristics of the collected fingerprints to achieve fingerprint unlocking, accessing application locks, taking photos with fingerprints, answering calls with fingerprints, etc.

[0204] Temperature sensor 180J is used to detect temperature. In some embodiments, electronic device 100 uses the temperature detected by temperature sensor 180J to execute a temperature handling strategy. For example, when the temperature reported by temperature sensor 180J exceeds a threshold, electronic device 100 performs thermal protection by reducing the performance of a processor located near temperature sensor 180J to reduce power consumption. In other embodiments, when the temperature is below another threshold, electronic device 100 heats battery 142 to prevent abnormal shutdown of electronic device 100 due to low temperature. In still other embodiments, when the temperature is below yet another threshold, electronic device 100 boosts the output voltage of battery 142 to prevent abnormal shutdown due to low temperature.

[0205] Touch sensor 180K, also known as a "touch device," can be located on display screen 194. The touch sensor 180K and display screen 194 together form a touchscreen, also known as a "touchscreen." Touch sensor 180K detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 194. In other embodiments, touch sensor 180K may also be located on the surface of electronic device 100, in a different position than display screen 194.

[0206] The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire vibration signals from the vibrating bone segments of the human vocal cords. The bone conduction sensor 180M can also contact the human pulse to receive blood pressure signals. In some embodiments, the bone conduction sensor 180M can also be incorporated into headphones to form bone conduction headphones. The audio module 170 can parse the voice signals from the vibrating bone segments of the vocal cords acquired by the bone conduction sensor 180M to realize voice functionality. The application processor can parse heart rate information from the blood pressure signals acquired by the bone conduction sensor 180M to realize heart rate detection functionality.

[0207] Buttons 190 include a power button, volume buttons, etc. Buttons 190 can be mechanical buttons or touch-sensitive buttons. Electronic device 100 can receive button input and generate key signal inputs related to user settings and function control of electronic device 100.

[0208] Motor 191 can generate vibration alerts. Motor 191 can be used for incoming call vibration alerts or for touch vibration feedback. For example, different vibration feedback effects can correspond to touch operations performed on different applications (such as taking photos, playing audio, etc.). Motor 191 can also correspond to different vibration feedback effects for touch operations performed on different areas of the display screen 194. Different application scenarios (such as time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also be customized.

[0209] Indicator 192 can be an indicator light, used to indicate charging status, power changes, or to indicate messages, missed calls, notifications, etc.

[0210] The SIM card interface 195 is used to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 195 to make contact with and separate from the electronic device 100. The electronic device 100 can support one or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 simultaneously. The multiple cards can be of the same or different types. The SIM card interface 195 is also compatible with different types of SIM cards. The SIM card interface 195 is also compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to realize functions such as calls and data communication. In some embodiments, the electronic device 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

[0211] This embodiment also provides a computer storage medium storing computer instructions. When the computer instructions are executed on the electronic device 100, the electronic device 100 performs the above-mentioned related method steps to implement the physiological detection signal quality assessment method in the above embodiment.

[0212] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the physiological detection signal quality assessment method in the above embodiment.

[0213] In addition, embodiments of this application also provide an apparatus, which may specifically be a chip, component or module. The apparatus may include a connected processor and a memory; wherein the memory is used to store computer execution instructions, and when the apparatus is running, the processor may execute the computer execution instructions stored in the memory to cause the chip to execute the physiological detection signal quality assessment method in the above method embodiments.

[0214] In this embodiment, the electronic device, computer storage medium, computer program product or chip are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding method provided above, and will not be repeated here.

[0215] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0216] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0217] The unit described as a separate component may or may not be physically separate. The component shown as a unit can be one physical unit or multiple physical units, that is, it can be located in one place or distributed in multiple different places. Some or all of the units can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0218] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0219] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially or in other words, the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for evaluating the quality of physiological detection signals, characterized in that, The method includes: Acquire sample datasets collected by the detection equipment in different scenarios; Select sample data from a scene from the sample dataset, and divide the sample data into physiological data and PPG data; Calculating the energy percentage of the physiological data based on the physiological data and the PPG data includes: performing a short-time Fourier transform on the PPG data to obtain the power spectral density data of the PPG data, wherein the window type of the short-time Fourier transform is a Hamming window; determining the time points corresponding to the center positions of multiple Hamming windows, and obtaining the physiological data corresponding to each time point; converting the physiological data into a frequency range; calculating the energy percentage corresponding to the frequency range based on the power spectral density data; and calculating the average of the multiple energy percentages corresponding to the frequency ranges of multiple physiological data to obtain the energy percentage of the physiological data. Based on the energy proportion of the physiological data corresponding to different scenarios, the quality assessment parameters of sample data for multiple classification scenarios are calculated respectively. The quality of the physiological detection signals collected by the detection device is evaluated based on the quality assessment parameters.

2. The physiological detection signal quality assessment method as described in claim 1, characterized in that, The sample data collected by the testing equipment in different scenarios includes: Multiple preset scenarios can be set based on tags; Connect the detection device to the data acquisition terminal; The detection device collects data based on a single preset scenario to generate single sample data. Based on the sample data of the multiple preset scenarios, output a multi-scenario sample dataset.

3. The physiological detection signal quality assessment method as described in claim 1, characterized in that, Calculating the energy percentage of the physiological data based on the physiological data and the PPG data also includes: Output a list of energy percentages of the multiple physiological data in the sample data based on the energy percentages corresponding to the frequency ranges of the multiple physiological data.

4. The physiological detection signal quality assessment method as described in claim 3, characterized in that, Calculating the energy percentage corresponding to the frequency range based on the power spectral density data includes: The energy percentage corresponding to the frequency range is obtained by dividing the sum of the energy values ​​corresponding to multiple frequency values ​​within the frequency range by the sum of the energy values ​​corresponding to all frequency values.

5. The physiological detection signal quality assessment method as described in claim 3, characterized in that, Calculating the energy percentages of multiple frequencies corresponding to multiple physiological data based on the physiological data and the PPG data also includes: The power spectral density data is interpolated using either time-domain zero-padding or frequency-domain filling.

6. The physiological detection signal quality assessment method as described in claim 3, characterized in that, Calculating the energy percentages of multiple frequencies corresponding to multiple physiological data based on the physiological data and the PPG data also includes: Remove abnormal data from the energy percentage list of the aforementioned physiological data.

7. The physiological detection signal quality assessment method as described in claim 6, characterized in that, Abnormal data to be removed from the energy percentage list of the aforementioned physiological data include: Calculate the upper quartile Q1 in the energy percentage list. E and lower quartile Q3 E ; Calculate the upper threshold U1 and lower threshold U2 of the abnormal data; Data in the energy percentage list that is less than or equal to the lower threshold or greater than or equal to the upper threshold is identified as abnormal data. Output a list of energy percentages after removing abnormal data.

8. The physiological detection signal quality assessment method as described in claim 7, characterized in that: The upper limit threshold The lower threshold a and b are the upper quartiles Q1 E and lower quartile Q3 E The coefficient.

9. The physiological detection signal quality assessment method as described in claim 1, characterized in that: The quality assessment parameters include the lower quartile, median, and upper quartile of the energy percentage of physiological data in multiple scenarios within the classification scenario.

10. The physiological detection signal quality assessment method as described in claim 9, characterized in that, Based on the energy proportion of the physiological data corresponding to different scenarios, quality assessment parameters for sample data in multiple classification scenarios are calculated, including: The different scenarios are classified to determine the multiple classified scenarios; Based on the analysis target, obtain the energy proportion of multiple physiological data corresponding to the classification scenario for at least two evaluation dimensions; Calculate the lower quartile, median, and upper quartile of the physiological data energy proportions for the classification scenarios corresponding to the at least two evaluation dimensions.

11. The physiological detection signal quality assessment method as described in claim 10, characterized in that, The quality assessment of the physiological detection signals acquired by the detection device based on the aforementioned quality assessment parameters includes: The quality of the physiological detection signal collected by the detection device is determined by the ratio of the increase or decrease of the quality assessment parameter of the classification scenario corresponding to the first assessment dimension relative to the quality assessment parameter of the classification scenario corresponding to the second assessment dimension.

12. The physiological detection signal quality assessment method as described in claim 11, characterized in that, Determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the proportion of improvement or decrease in the quality assessment parameters of the classification scenario corresponding to the first assessment dimension relative to the quality assessment parameters of the classification scenario corresponding to the second assessment dimension includes: Calculate the lower quartile increase, median increase, and upper quartile increase of the energy proportion of multiple physiological data in the classification scenario corresponding to the first evaluation dimension relative to the energy proportion of multiple physiological data in the classification scenario corresponding to the second evaluation dimension. The quality of the physiological detection signal collected by the detection device is determined based on the lower quartile increase ratio, the median increase ratio, and the upper quartile increase ratio.

13. The physiological detection signal quality assessment method as described in claim 12, characterized in that, Determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile elevation ratio, median elevation ratio, and upper quartile elevation ratio includes: Determine whether the increase ratio of the lower quartile, the increase ratio of the median, and the increase ratio of the upper quartile are greater than 0; If the improvement ratio of the lower quartile, the improvement ratio of the median, and the improvement ratio of the upper quartile are determined to be greater than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is improved relative to the second evaluation dimension. If it is determined that the increase ratio of the lower quartile, the increase ratio of the median, and the increase ratio of the upper quartile are all equal to 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension has not changed relative to the second evaluation dimension. If any one of the following ratios is determined to be less than 0: the lower quartile increase ratio, the median increase ratio, and the upper quartile increase ratio, then the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is determined to be lower than that under the second evaluation dimension.

14. The physiological detection signal quality assessment method as described in claim 12, characterized in that, Determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile elevation ratio, median elevation ratio, and upper quartile elevation ratio includes: Calculate the average of the lower quartile increase rate, the median increase rate, and the upper quartile increase rate; Determine whether the average value is greater than 0; If the average value is determined to be greater than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is improved relative to the second evaluation dimension; If the average value is determined to be 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension has not changed relative to the second evaluation dimension; If the average value is determined to be less than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is reduced relative to the second evaluation dimension.

15. The physiological detection signal quality assessment method as described in claim 12, characterized in that, Determining whether the quality of the physiological detection signal acquired by the detection device has improved or decreased based on the lower quartile elevation ratio, median elevation ratio, and upper quartile elevation ratio includes: Set the weight values ​​for the lower quartile increase ratio, the median increase ratio, and the upper quartile increase ratio, respectively; The sum of the lower quartile increase ratio, median increase ratio, and upper quartile increase ratio is calculated based on the weight values ​​of the lower quartile increase ratio, median increase ratio, and upper quartile increase ratio. Determine whether the sum of the lower quartile increase ratio, the median increase ratio, and the upper quartile increase ratio is greater than 0; If the sum of the lower quartile improvement ratio, the median improvement ratio, and the upper quartile improvement ratio is determined to be greater than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is improved relative to the second evaluation dimension. If the sum of the lower quartile increase ratio, the median increase ratio, and the upper quartile increase ratio is determined to be 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension has not changed relative to the second evaluation dimension. If the sum of the lower quartile improvement ratio, median improvement ratio, and upper quartile improvement ratio is determined to be less than 0, it is determined that the quality of the physiological detection signal collected by the detection device under the first evaluation dimension is reduced relative to the second evaluation dimension.

16. The physiological detection signal quality assessment method as described in claim 1, characterized in that, The method further includes: The PPG data in the sample data is upsampled.

17. The physiological detection signal quality assessment method as described in claim 1, characterized in that, The method further includes: Filter out low-frequency and / or high-frequency noise in the PPG data of the sample data.

18. The physiological detection signal quality assessment method as described in claim 17, characterized in that, Filtering out low-frequency and / or high-frequency noise from the PPG data in the sample data includes: The upper and lower threshold values ​​of the bandpass frequency are set using an f-order bandpass filter; The PPG data is input into the bandpass filter, and the bandpass filter filters out low-frequency and / or high-frequency noise in the PPG data based on the upper and lower thresholds.

19. An electronic device, characterized in that, The electronic device includes a memory and a processor: The memory is used to store program instructions; The processor is configured to read and execute the program instructions stored in the memory, and when the program instructions are executed by the processor, cause the electronic device to perform the physiological detection signal quality assessment method as described in any one of claims 1 to 18.

20. A computer storage medium, characterized in that, The computer storage medium stores program instructions that, when executed on an electronic device, cause the electronic device to perform the physiological detection signal quality assessment method as described in any one of claims 1 to 18.

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