Data processing method and device and storage medium

Abstract

The embodiment of the invention provides a data processing method and device and a storage medium. The method is applied to the wearable electronic equipment, and comprises the following steps: collecting biological characteristic data based on a first sampling frequency; dividing the biological characteristic data into a plurality of windows according to a preset sampling proportion; determining whether each window contains a peak point or a valley point; if one window contains a peak point or a valley point, taking the numerical value of the peak point or the numerical value of the valley point as a sampling value of the window; if one window does not contain the peak point or the valley point, taking the average value of the window as the sampling value of the window; and generating a downsampled oscillogram based on the sampling value of each window. According to the embodiment of the invention, data feature loss caused by sampling can be reduced, and the accuracy of the sampled oscillogram is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing, and in particular to a data processing method, device and storage medium. Background Art

[0002] As the application scope of wearable electronic devices gradually expands, the collection and monitoring of human characteristic data has become one of the indispensable functions of wearable electronic devices, such as the collection and display of biometric data such as electrocardiogram (ECG), heart rate, and respiratory rate.

[0003] Since the display screen of wearable electronic devices is often limited by the size of the wearing position, the above biometric data is difficult to fully display. Therefore, the biometric data collected by wearable electronic devices needs to be sampled, compressed, and processed before the processed data is displayed.

[0004] However, data after sampling, compression and other processing often have certain distortions, and some important data features in the biometric data may be lost and not displayed, which greatly reduces the accuracy of the displayed data and leads to a poor user experience. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the present disclosure provides a data processing method, device and storage medium to overcome the problem that the accuracy of the existing data is reduced after downsampling and post-processing.

[0006] According to a first aspect of an embodiment of the present disclosure, a data processing method is provided, the method being applied to a wearable electronic device, comprising:

[0007] Based on the first sampling frequency, collecting biometric data;

[0008] Dividing the biometric data into a plurality of windows according to a preset sampling ratio;

[0009] Determine whether each of the windows contains a peak point or a valley point;

[0010] If a window contains a peak point or a valley point, the value of the peak point or the value of the valley point is used as the sampling value of the window;

[0011] If a window does not contain a peak point or a valley point, the average value of the window is used as the sampling value of the window;

[0012] Based on the sampling values ​​of each window, a downsampled waveform is generated.

[0013] In some embodiments, determining whether each of the windows contains a peak point or a valley point includes:

[0014] Determining whether the biometric data in each of the windows is monotonic;

[0015] If the biometric data in the window is not monotonic, the window contains a peak point or a valley point.

[0016] In some embodiments, the determining whether each of the windows contains a peak point or a valley point further includes:

[0017] If the window is the first window and the window is monotonic, then the window does not contain a peak point or a valley point;

[0018] If the window has monotonicity and the previous window of the window has opposite monotonicity, then the window contains a peak point or a valley point.

[0019] In some embodiments, the method further comprises:

[0020] The first value in the window that is monotonic and contains the peak point or the estimated point is determined as the value of the peak point or the valley point.

[0021] In some embodiments, determining whether the biometric data in each of the windows is monotonic includes:

[0022] Performing a first-order difference operation on the biometric data in the window to obtain a first-order difference sequence;

[0023] If the values ​​of the first-order difference sequence are all greater than or equal to zero, or are all less than or equal to zero, it is determined that the biometric data in the window is monotonic.

[0024] In some embodiments, the method further comprises:

[0025] The wearable electronic device displays the waveform after downsampling.

[0026] In some embodiments, the biometric data includes electrocardiogram (ECG) data.

[0027] According to a second aspect of an embodiment of the present disclosure, a data processing device is provided, wherein the device is applied to a wearable electronic device, including:

[0028] A collection module configured to collect biometric data based on a first sampling frequency;

[0029] A division module, configured to divide the biometric data into a plurality of windows according to a preset sampling ratio;

[0030] A first determination module is configured to determine whether each window contains a peak point or a valley point;

[0031] A first sampling module is configured to use the value of the peak point or the value of the valley point as the sampling value of the window if a window contains a peak point or a valley point;

[0032] A second sampling module is configured to use an average value of a window as a sampling value of the window if a window does not contain a peak point or a valley point;

[0033] The generation module is configured to generate a downsampled waveform based on the sampling values ​​of each window.

[0034] In some embodiments, the first determining module includes:

[0035] A first determination submodule is configured to determine whether the biometric data in each of the windows is monotonic;

[0036] The second determination submodule is configured to determine whether the window contains a peak point or a valley point if the biometric feature data in the window is not monotonic.

[0037] In some embodiments, the second determining submodule is specifically configured as follows:

[0038] If the window is the first window and the window is monotonic, then the window does not contain a peak point or a valley point;

[0039] If the window has monotonicity and the previous window of the window has opposite monotonicity, then the window contains a peak point or a valley point.

[0040] In some embodiments, the apparatus further comprises:

[0041] The second determination module is configured to determine the first value in the window that is monotonic and contains the peak point or the estimated point as the value of the peak point or the valley point.

[0042] In some embodiments, the first determining submodule is specifically configured as follows:

[0043] Performing a first-order difference operation on the biometric data in the window to obtain a first-order difference sequence;

[0044] If the values ​​of the first-order difference sequence are all greater than or equal to zero, or are all less than or equal to zero, it is determined that the biometric data in the window is monotonic.

[0045] In some embodiments, the apparatus further comprises:

[0046] The display module is configured to display the waveform graph after downsampling on the wearable electronic device.

[0047] In some embodiments, the biometric data includes electrocardiogram (ECG) data.

[0048] According to a third aspect of an embodiment of the present disclosure, there is provided a data processing device, comprising: a processor; and a memory for storing processor executable instructions; wherein the processor is configured to: implement the steps in any one of the above-mentioned data processing methods when executed.

[0049] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided, and when instructions in the storage medium are executed by a processor of an information processing device, the device is enabled to perform the steps in any one of the above-mentioned data processing methods.

[0050] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0051] The disclosed embodiment provides a data processing method, through which the collected biometric data is divided into windows according to a preset sampling ratio, and then the peak point or valley point in each window is determined as the sampling value, or the mean of the data in the window is used as the sampling value. In this way, the characteristic data of the peak point or valley point can be effectively retained, the loss of data features caused by sampling can be reduced, the accuracy of the waveform after sampling can be improved, and the user experience of the wearable electronic device can be improved.

[0052] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0054] Figure 1 The data processing method according to an exemplary embodiment is shown in FIG. Figure 1 ;

[0055] Figure 2 is a structural block diagram of a data processing device according to an exemplary embodiment;

[0056] Figure 3 is a schematic diagram of an electrocardiogram waveform according to an exemplary embodiment;

[0057] Figure 4 The data processing method according to an exemplary embodiment is shown in FIG. Figure 2 ;

[0058] Figure 5 A hardware structure frame of a data processing device according to an exemplary embodiment is shown Figure 1 ;

[0059] Figure 6 A hardware structure frame of a data processing device according to an exemplary embodiment is shown Figure 2 . DETAILED DESCRIPTION

[0060] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0061] The following is a detailed description:

[0062] Figure 1 is a flow chart of a data processing method according to an exemplary embodiment. Figure 1 As shown, a data processing method is provided, which is applied to a wearable electronic device, comprising:

[0063] In step 101, biometric data is collected based on a first sampling frequency;

[0064] In step 102, the biometric data is divided into a plurality of windows according to a preset sampling ratio;

[0065] In step 103, it is determined whether each window contains a peak point or a valley point;

[0066] In step 104, if a window contains a peak point or a valley point, the value of the peak point or the value of the valley point is used as the sampling value of the window;

[0067] In step 105, if a window does not contain a peak point or a valley point, the average value of the window is used as the sampling value of the window;

[0068] In step 106, a down-sampled waveform is generated based on the sampling values ​​of each window.

[0069] Here, the first sampling frequency is a sampling frequency used to obtain the original biometric data. In order to ensure that the sampled data is as accurate as possible, the first sampling frequency may be a relatively high frequency, and illustratively, the first sampling frequency may be 500 Hz, 1000 Hz, etc. The biometric data obtained by the first sampling frequency may include various important feature data in the biological signal. Therefore, in some embodiments, the collected biometric data may be used to assist medical observation.

[0070] In some embodiments, the biometric data is electrocardiogram (ECG).

[0071] Here, a certain sampling ratio can be set according to the needs to downsample the above-mentioned biometric data, that is, the biometric data obtained by the above-mentioned first sampling frequency is downsampled to a set of data with a lower sampling frequency for processing and display of wearable electronic devices, etc. This can greatly reduce the amount of data calculation of wearable electronic devices. Exemplarily, the above-mentioned sampling ratio can be determined based on the display pixels of the display screen of the wearable electronic device; or the above-mentioned sampling ratio can be determined according to the data computing capability of the wearable electronic device. In addition, the above-mentioned sampling ratio can also be determined according to the observation requirements of the sampled biometric data. For example, in order to accurately observe the QRS wave in the ECG signal, the sampling ratio can be determined according to the usual waveform of the QRS wave, at least so that the QRS wave is distributed in different windows, and after sampling, the Q wave, R wave and S wave can all be displayed without losing the waveform due to sampling.

[0072] For example, the sampling ratio is set to 10:1, that is, among the biometric data collected at the first sampling frequency, 1 data is sampled for every 10 data; for another example, the sampling ratio is set to 50:1, that is, among the biometric data collected at the first sampling frequency, 1 data is sampled for every 50 data.

[0073] In the above step 102, all data are divided into multiple windows, i.e., multiple arrays, at a determined sampling ratio, and the window length of each array is the value of the above sampling ratio. For example, if the sampling ratio is 10:1, the window length is 10, i.e., each window contains 10 data; if the sampling ratio is 50:1, the window length is 50, i.e., each window contains 50 data.

[0074] Next, a sampling value is determined for each window, and the set of these sampling values ​​is the biometric data after downsampling.

[0075] In the embodiment of the present disclosure, it is considered that each window contains two different situations, the first one is having a peak point or a valley point; the second one is not having a peak point or a valley point. For the second situation, two averaging methods can be used to determine the sampling value, or the median or the middle data can be used as the sampling value. For the first situation, it is necessary to confirm the value of the peak point or the valley point, and use the value of the peak point or the valley point as the sampling value of the window.

[0076] Considering that in the waveform of biometric data, peak points or valley points usually have special observation significance, for example, the QRS complex in the ECG signal can be used to reflect the changes in the depolarization potential and time of the left and right ventricles, that is, the new changes in the contraction process of the left and right ventricles. A QRS complex appears in every heartbeat, which also represents a contraction of the heart. QRS consists of three waves, namely Q wave, R wave and S wave. The positive wave above the horizontal line is the R wave. There is a negative wave before the R wave, which is the Q wave. The S wave is the negative wave after the R wave. Therefore, when observing the Q wave, R wave, and S wave, if the peak or trough is offset due to sampling, the waveform will be inaccurate, thereby affecting the display of the ECG waveform and causing misjudgment of ECG abnormalities.

[0077] Therefore, the embodiment of the present disclosure adopts the above method to determine the peak point or valley point of each window, thereby retaining representative feature data in the biometric data, thereby reducing waveform distortion caused by sampling.

[0078] In the embodiment of the present disclosure, it is possible to detect whether each window contains a peak point or a valley point. If it does not contain a peak point or a valley point, the sampling value can be directly determined by using the above-mentioned method such as taking the mean or taking the median. If it contains a peak point or a valley point, the value of the peak point or the valley point is determined, and then the value is used as the sampling value.

[0079] In some embodiments, determining whether each of the windows contains a peak point or a valley point includes:

[0080] Determining whether the biometric data in each of the windows is monotonic;

[0081] If the biometric data in the window is not monotonic, the window contains a peak point or a valley point.

[0082] The method for determining whether each window contains a peak point or a valley point can be determined by determining whether the data is monotonous, that is, determining whether the data in the window is monotonically rising or monotonically falling. If the data in the window is monotonically rising, it means that the last data is the maximum value and the first data is the minimum value, so there is no peak or valley in the window. If the data in the window is monotonically falling, it means that the last data is the minimum value and the first data is the maximum value, so there is no peak or valley in the window.

[0083] If the data in the window is not monotonic, for example, if the data first drops and then rises, it means there is a trough; if the data first rises and then drops, it means there is a peak. Therefore, the window is determined to have a peak point or a valley point.

[0084] In some embodiments, determining the value of the peak point or the valley point includes:

[0085] The value in the window that is greater than the values ​​of the biometric data on both sides is determined as the value of the peak point; or the value in the window that is less than the values ​​of the biometric data on both sides is determined as the value of the valley point.

[0086] In the embodiment of the present disclosure, the peak point or valley point of the data in the window can be determined by comparing the sizes of adjacent data in sequence. If a data is larger than the previous data and larger than the next data, then the data is a peak point. At this time, the value of the point can be determined as the value of the peak point; if a data is smaller than the previous data and smaller than the next data, then the data is a valley point. At this time, the value of the point can be determined as the value of the valley point.

[0087] In some embodiments, the determining whether each of the windows contains a peak point or a valley point further includes:

[0088] If the window is the first window and the window is monotonic, then the window does not contain a peak point or a valley point;

[0089] If the window has monotonicity and the previous window of the window has opposite monotonicity, then the window contains a peak point or a valley point.

[0090] For the above-mentioned monotonic windows, there is also a special case, that is, the peak point or valley point is exactly at the junction of two windows. Therefore, it can be stipulated here that if a window is monotonic, the previous window of the window is also monotonic, and the monotonicity of the previous window is opposite to the monotonicity of the window, then the first point of the window is considered to be a peak point or valley point. If the window has no previous window, that is, the window is the first window, then it is considered that the window does not contain a peak point or valley point.

[0091] For example, if a window is monotonically increasing and its previous window is monotonically decreasing, it means that the first data of the window is the minimum value relative to other data in the window and the data in the previous window, so the first data of the window is a valley point. On the contrary, if a window is monotonically decreasing and its previous window is monotonically increasing, it means that the first data of the window is the maximum value relative to other data in the window and the data in the previous window, so the first data of the window is a peak point.

[0092] In some embodiments, determining the value of the peak point or the valley point includes:

[0093] The first value in the window that is monotonic and contains the peak point or the estimated point is determined as the value of the peak point or the valley point.

[0094] In some embodiments, determining whether the biometric data in each of the windows is monotonic includes:

[0095] Performing a first-order difference operation on the biometric data in the window to obtain a first-order difference sequence;

[0096] If the values ​​of the first-order difference sequence are all greater than or equal to zero, or are all less than or equal to zero, it is determined that the biometric data in the window is monotonic.

[0097] The first-order difference operation is to subtract the previous data from the next data, and then calculate each data in turn to get the sequence. If the data in the window is monotonic, that is, the data increases or decreases in sequence, then the values ​​of the first-order difference sequence are all positive or all negative. Of course, if the first-order difference sequence includes zero values, it means that two adjacent data in the window are equal in value, and other values ​​in the first-order difference sequence are all positive or all negative. In this case, the window is also considered to be monotonic.

[0098] In some embodiments, the method further comprises:

[0099] The wearable electronic device displays the waveform after downsampling.

[0100] Exemplarily, the wearable electronic device is a smart watch, a smart bracelet, a wearable real-time ECG monitoring device or other wearable device, which, after detecting biometric data such as an electrocardiogram, displays a waveform corresponding to the data after downsampling processing in the above embodiment, so as to facilitate real-time observation by the user.

[0101] like Figure 2 As shown, the embodiment of the present disclosure further provides a data processing device 100, which is applied to a wearable electronic device, including:

[0102] The collection module 110 is configured to collect biometric data based on a first sampling frequency;

[0103] A division module 120, configured to divide the biometric data into a plurality of windows according to a preset sampling ratio;

[0104] A first determination module 130 is configured to determine whether each window contains a peak point or a valley point;

[0105] The first sampling module 140 is configured to use the value of the peak point or the value of the valley point as the sampling value of the window if a window contains a peak point or a valley point;

[0106] The second sampling module 150 is configured to use the average value of a window as the sampling value of the window if a window does not contain a peak point or a valley point;

[0107] The generating module 160 is configured to generate a downsampled waveform diagram based on the sampling values ​​of each window.

[0108] In some embodiments, the first determining module includes:

[0109] A first determination submodule is configured to determine whether the biometric data in each of the windows is monotonic;

[0110] The second determination submodule is configured to determine whether the window contains a peak point or a valley point if the biometric feature data in the window is not monotonic.

[0111] In some embodiments, the second determining submodule is specifically configured as follows:

[0112] If the window is the first window and the window is monotonic, then the window does not contain a peak point or a valley point;

[0113] If the window has monotonicity and the previous window of the window has opposite monotonicity, then the window contains a peak point or a valley point.

[0114] In some embodiments, the apparatus further comprises:

[0115] The second determination module is configured to determine the first value in the window that is monotonic and contains the peak point or the estimated point as the value of the peak point or the valley point.

[0116] In some embodiments, the first determining submodule is specifically configured as follows:

[0117] Performing a first-order difference operation on the biometric data in the window to obtain a first-order difference sequence;

[0118] If the values ​​of the first-order difference sequence are all greater than or equal to zero, or are all less than or equal to zero, it is determined that the biometric data in the window is monotonic.

[0119] In some embodiments, the apparatus further comprises:

[0120] The display module is configured to display the waveform graph after downsampling on the wearable electronic device.

[0121] In some embodiments, the biometric data includes electrocardiogram (ECG) data.

[0122] The present disclosure also provides the following examples:

[0123] ECG (electrocardiograph) wearable devices are one of the important development directions of wearable health monitoring technology. With the advancement of technology, the ECG collection and analysis functions of wearable devices are constantly improving.

[0124] The current mainstream ECG wearable devices are mostly smart watches or smart bracelets, which can realize dynamic ECG monitoring and provide functions such as heart rate variability analysis. These devices collect ECG data through wrist sensors.

[0125] During the ECG measurement process, the product can be equipped with a real-time electrocardiogram display function to show the user the real-time measurement process.

[0126] The sampling frequency of ECG is relatively high, and typical sampling frequencies are 500Hz, 1000Hz, etc. In the process of real-time acquisition, if all are displayed, it will take up a very large amount of calculation. Due to the limited display pixels on the watch side, the subjective perception of high-sampling signals and low-sampling signals is basically the same. Therefore, the products on the market basically use downsampling to display ECG signals in real time on the watch side.

[0127] The QRS complex is a typical wave group feature of the ECG signal. Its R wave is a significant peak point, while Q and S are valley points on its left and right. In the process of downsampling, if the sampling point is offset from the exact position of the R wave, the peak value of the R wave displayed on the electrocardiogram will be different. If the sliding average or time-frequency domain conversion sampling method is used, this result will also be brought about. As long as the peak value is not judged but processed uniformly, this result will occur.

[0128] like Figure 3As shown in the figure, the first row is an ECG signal with a consistent R wave amplitude, all of which are 0.7mV. In the normally collected ECG signal, as long as the subject's movements remain consistent and the heart rhythm is regular, the amplitude of the R wave will be relatively consistent. If a simple sampling or averaging method is adopted, the situation in the second row will appear: the amplitude of the R wave is reduced and the height is inconsistent. For auxiliary medical observation, this will affect the professionalism of the waveform display and affect the medical diagnosis results; for ordinary users, the subjective visual experience is impaired.

[0129] Figure 3 The third line is the effect after processing using the embodiment of the present disclosure. Among them, the offset of the peak and the trough are corrected by the method of the embodiment of the present disclosure. It can be seen that the processed R wave peak retains the height of the R wave peak before downsampling, and they are consistent, retaining the QRS wave group morphological characteristics under the sampling display, with higher professionalism and better user experience.

[0130] The process of the data processing method provided by the embodiment of the present disclosure is as follows: Figure 4 As shown:

[0131] In step 201, an original ECG signal is acquired, and its sampling frequency is Fs.

[0132] In step 202, the downsampling ratio k required for ECG display is set, that is, the ECG display frequency after downsampling is Fs / k. Generally, k can be 10, 50, etc.

[0133] In step 203, for each window Si=[Ai0, Ai1, Ai2, ..., Aik-1], the following processing is performed so that only one processed sampling value Ei is returned after the window is processed.

[0134] Step 203a, determine whether the numerical curve in the window is monotonic. Perform first-order difference on the window Si = [Ai0, Ai1, Ai2, ..., Aik-1] to obtain the first-order difference sequence Di = [Di1, Di2, ..., Dik-1], where Din = Ain-Ain-1, n = 1, 2, 3, ..., k-1. For the first-order difference sequence Di = [Di1, Di2, ..., Dik-1], if all are greater than or equal to 0, it means that the window is monotonically rising and monotonic; if all are less than or equal to 0, it means that the window is monotonically decreasing and monotonic; if the window does not meet the first two monotonic conditions, it means that the window is not monotonic.

[0135] Step 203b, find the peak point in the window. The definition of the peak point is as follows: if the point is the first point or the last point in the window, it is not considered a peak point; if the point is neither the first point nor the last point in the window, it is considered a peak point only when the value of the point is greater than the point to its left and greater than the point to its right.

[0136] Step 203c: if there is a peak point in the window, the returned sample value Ei is the peak point value with the largest value. Otherwise, go to step 203d.

[0137] Step 203d, determine the monotonicity relationship between the window and the previous window. If the window is the first window and there is no previous window, go to step 203e. If the window is not the first window, then: if the monotonicity of the window and the previous window are opposite (i.e., one is monotonically rising and the other is monotonically falling), it means that the first point of the window is a peak point or a valley point, and return the first point of the window as the sampling value Ei.

[0138] Step 203e, calculating the mean value in the window as the sampling value Ei.

[0139] In step 204, the sampling values ​​Ei of each window are obtained, added to the sampling result sequence according to the time sequence, and the down-sampled ECG waveform is drawn.

[0140] Through the technical solution, while displaying the ECG signal through downsampling and reducing real-time computing processing, it reflects the height of the R wave peak before downsampling and retains the morphological characteristics of the QRS complex under the sampling display, with higher professionalism and better user experience.

[0141] Figure 5 is a block diagram of a data processing device 900 according to an exemplary embodiment. For example, the device 900 may be a mobile phone, a mobile computer, etc.

[0142] Reference Figure 5 , the device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output (I / O) interface 912 , a sensor component 914 , and a communication component 916 .

[0143] The processing component 902 generally controls the overall operation of the device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 902 may include one or more modules to facilitate interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate interaction between the multimedia component 908 and the processing component 902.

[0144] The memory 904 is configured to store various types of data to support operations on the device 900. Examples of such data include instructions for any application or method operating on the device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0145] The power supply component 906 provides power to the various components of the device 900. The power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 900.

[0146] The multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0147] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the device 900 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting audio signals.

[0148] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0149] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the device 900. For example, the sensor assembly 914 can detect the open / closed state of the device 900, the relative positioning of components, such as the display and keypad of the device 900, and the sensor assembly 914 can also detect the position change of the device 900 or a component of the device 900, the presence or absence of user contact with the device 900, the orientation or acceleration / deceleration of the device 900, and the temperature change of the device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0150] The communication component 916 is configured to facilitate wired or wireless communication between the device 900 and other devices. The device 900 can access a wireless network based on a communication standard, such as Wi-Fi, 4G or 5G, or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0151] In an exemplary embodiment, the apparatus 900 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0152] In an exemplary embodiment, a terminal is also provided, which may include the modules or components in the above-mentioned information processing device, the terminal also includes a first security chip, and the terminal may also include a second security chip. Exemplarily, the terminal may be a smart phone, a tablet computer, a computer, a smart wearable device, and a vehicle-mounted device.

[0153] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, and the instructions can be executed by the processor 920 of the device 900 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0154] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an information processing device, enables the data processing device to execute the data processing method in any of the above embodiments.

[0155] Figure 6 1900 is a hardware structure block diagram of an information processing device according to an exemplary embodiment. For example, the device 1900 may be provided as a server. Figure 6 , the device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions that can be executed by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the data processing method in any of the above embodiments.

[0156] The device 1900 may also include a power supply component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input / output (I / O) interface 1958. The device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™, or the like.

[0157] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present disclosure. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present disclosure, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure. The serial numbers of the embodiments of the present disclosure are for description only and do not represent the advantages and disadvantages of the embodiments.

[0158] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0159] In the several embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0160] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0161] In addition, all functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may be separately configured as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0162] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0163] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: The method is applied to a wearable electronic device, comprising: Based on the first sampling frequency, collecting biometric data; Dividing the biometric data into a plurality of windows according to a preset sampling ratio; Determine whether each of the windows contains a peak point or a valley point; If a window contains a peak point or a valley point, the value of the peak point or the value of the valley point is used as the sampling value of the window; If a window does not contain a peak point or a valley point, the average value of the window is used as the sampling value of the window; Based on the sampling values ​​of each window, a downsampled waveform is generated.

2. The method according to claim 1, characterized in that The step of determining whether each window contains a peak point or a valley point comprises: Determining whether the biometric data in each of the windows is monotonic; If the biometric data in the window is not monotonic, the window contains a peak point or a valley point.

3. The method according to claim 2, characterized in that The determining whether each of the windows contains a peak point or a valley point further includes: If the window is the first window and the window is monotonic, then the window does not contain a peak point or a valley point; If the window has monotonicity and the previous window of the window has opposite monotonicity, then the window contains a peak point or a valley point.

4. The method according to claim 3, characterized in that The method further comprises: The first value in the window that is monotonic and contains the peak point or the estimated point is determined as the value of the peak point or the valley point.

5. The method according to claim 2, characterized in that: The determining whether the biometric data in each of the windows is monotonic comprises: Performing a first-order difference operation on the biometric data in the window to obtain a first-order difference sequence; If the values ​​of the first-order difference sequence are all greater than or equal to zero, or are all less than or equal to zero, it is determined that the biometric data in the window is monotonic.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The wearable electronic device displays the waveform after downsampling.

7. The method according to any one of claims 1 to 5, characterized in that: The biometric data includes: electrocardiogram data ECG.

8. A data processing device, characterized in that: The device is applied to a wearable electronic device, comprising: A collection module configured to collect biometric data based on a first sampling frequency; A division module, configured to divide the biometric data into a plurality of windows according to a preset sampling ratio; A first determination module is configured to determine whether each of the windows contains a peak point or a valley point; A first sampling module is configured to use the value of the peak point or the value of the valley point as the sampling value of the window if a window contains a peak point or a valley point; A second sampling module is configured to use an average value of a window as a sampling value of the window if a window does not contain a peak point or a valley point; The generation module is configured to generate a downsampled waveform based on the sampling values ​​of each window.

9. A data processing device, characterized in that: include: processor; A memory for storing processor executable instructions; wherein the processor is configured to: implement the steps in any one of the data processing methods of claims 1 to 7 when executed.

10. A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an information processing device, the device is enabled to perform the steps in any one of the data processing methods of claims 1 to 7.

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