Signal processing method, chip and electronic equipment

Abstract

The invention relates to the technical field of integrated circuits, in particular to a signal processing method, a chip and electronic equipment, and the method comprises the steps: obtaining a signal quality state according to a first photoelectric detection signal; when the signal quality state is a stable state, obtaining physical sign parameters of the user according to the first photoelectric detection signal; when the signal quality state is an uncertain state, obtaining physical sign parameters of the user according to the first photoelectric detection signal and / or first motion data of the user; according to the mode, different physical sign signal acquisition modes are adapted according to the signal quality state, and when the signal quality of the first photoelectric detection signal is poor, the first motion data is introduced to participate in calculation of the physical sign parameters, so that the accuracy of physical sign parameter detection is improved.

Description

Technical Field

[0001] The present application relates to the field of integrated circuit technology, and in particular to a signal processing method, chip, and electronic device. Background Art

[0002] Currently, electronic devices can use, for example, photoplethysmography (PPG) signals to measure vital signs. A photoelectric sensor uses a light-emitting diode (LED) to emit light toward the skin. This light passes through the skin tissue, is absorbed by the bloodstream, and then reflected by the skin. The photoelectric sensor then receives the reflected light signal through a receiver. This reflected light signal is the PPG signal, and the intensity changes of the reflected light signal are used to detect vital signs, such as heart rate, blood pressure, or blood oxygen saturation.

[0003] In the related art, when calculating vital sign parameters, a relatively stable photoplethysmography signal is required. However, the spectrum of the photoplethysmography signal is weak during the user's exercise type conversion, and the spectrum of the photoplethysmography signal easily overlaps with the frequency of the combined acceleration when the user performs medium to high intensity exercise or certain irregular exercises, making it difficult to extract a relatively stable photoplethysmography signal. As a result, when a stable photoplethysmography signal cannot be obtained, the accuracy of using the photoplethysmography signal to detect vital sign parameters is not high. Summary of the Invention

[0004] In view of the above problems, the embodiments of the present application provide a signal processing method, a chip, and an electronic device to solve the technical problem of low accuracy in the above-mentioned vital sign parameter detection.

[0005] In a first aspect, an embodiment of the present application provides a signal processing method, including:

[0006] acquiring a signal quality status according to the first photoelectric detection signal;

[0007] When the signal quality state is a stable state, obtaining a user's vital sign parameter according to the first photoelectric detection signal;

[0008] When the signal quality state is an uncertain state, the user's vital sign parameters are obtained according to the first photoelectric detection signal and / or the user's first motion data, wherein the first motion data at least includes the user's motion type.

[0009] In a second aspect, an embodiment of the present application provides a chip, including:

[0010] A storage module for storing a signal processing program;

[0011] The processing module is used to implement the steps of the above-mentioned signal processing method when executing the signal processing program stored in the storage module.

[0012] In a third aspect, an embodiment of the present application provides an electronic device comprising the above-mentioned chip.

[0013] The signal processing method, chip and electronic device provided in the embodiments of the present application obtain the signal quality status based on the first photoelectric detection signal; when the signal quality status is a stable state, the user's vital sign parameters are obtained based on the first photoelectric detection signal; when the signal quality status is an uncertain state, the user's vital sign parameters are obtained based on the first photoelectric detection signal and / or the user's first motion data; through the above method, different vital sign signal acquisition methods are adapted according to the signal quality status, and when the signal quality of the first photoelectric detection signal is poor, the first motion data is introduced to participate in the calculation of the vital sign parameters, which is beneficial to improving the accuracy of vital sign parameter detection.

[0014] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The following diagram shows an application scenario of the signal processing method provided in an embodiment of the present application.

[0016] Figure 2 The figure shows a flow chart of the signal processing method provided in the embodiment of the present application.

[0017] Figure 3 A photoelectric detection frequency domain diagram of the first photoelectric detection signal in an embodiment of the present application is shown.

[0018] Figure 4 A schematic diagram showing spectrum aliasing of the first photodetection signal in an embodiment of the present application is shown.

[0019] Figure 5 A schematic diagram of the first photoelectric detection signal during the conversion of motion types in an embodiment of the present application is shown.

[0020] Figure 6 A schematic diagram of the structure of the chip provided in an embodiment of the present application is shown.

[0021] Figure 7 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0023] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0024] In the embodiments of the present application, it should be noted that, in this document, relational terms such as first and second, etc., are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0025] Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0026] In the description of the embodiments of this application, words such as "example" or "for example" are used to indicate an example, illustration, or description. Any embodiment or design described as "for example" or "for example" in the embodiments of this application is not to be construed as being preferred or having more advantages than another embodiment or design. The use of words such as "example" or "for example" is intended to clearly present relative concepts.

[0027] In addition, in the embodiments of the present application, "plurality" refers to two or more. In view of this, in the embodiments of the present application, "plurality" can also be understood as "at least two". "At least one" can be understood as one or more, for example, one, two, or more. For example, "including at least one" means including one, two, or more, and does not limit which ones are included. For example, "including at least one of A, B, and C" means including A, B, C, A and B, A and C, B and C, or A, B, and C.

[0028] It should be noted that in the embodiments of the present application, "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / ", unless otherwise specified, generally indicates that the associated objects are in an "or" relationship.

[0029] It should be noted that in the embodiments of the present application, "connection" can be understood as electrical connection, and the connection between two electrical components can be a direct or indirect connection between the two electrical components. For example, the connection between A and B can be either a direct connection between A and B or an indirect connection between A and B through one or more other electrical components.

[0030] The signal processing method provided in this application can be applied to Figure 1 In the wearable device 200 shown, the wearable device 200 includes a PPG sensor 201, an acceleration sensor 202 and a chip 100. The PPG sensor 201 is used to collect photoelectric detection signals, the acceleration sensor 202 is used to collect motion signals, and the chip 100 is used to receive photoelectric detection signals and motion signals, and execute the signal processing method to obtain the user's vital sign parameters based on the photoelectric detection signals and / or motion signals.

[0031] An embodiment of the present application provides a signal processing method. Figure 2 As shown, the signal processing method includes the following steps S10 to S30:

[0032] Step S10: Acquire a signal quality status according to the first photoelectric detection signal.

[0033] Among them, at least one signal feature can be extracted from the first photodetection signal, and the quality of the first photodetection signal can be evaluated based on the signal feature to determine the signal quality status of the first photodetection signal. Exemplarily, the first photodetection signal can be subjected to frequency domain analysis, and at least one peak of the frequency domain data corresponding to the first photodetection signal can be quality evaluated. If there is a high and clear peak, the quality of the first photodetection signal is good, and its signal quality status is stable; if the peaks are messy and the peaks are not high, the quality of the first photodetection signal is poor, and its signal quality status is uncertain. Exemplarily, the first photodetection signal can be a photoplethysmogram signal collected by a PPG sensor, or the first photodetection signal can be a signal obtained by preprocessing the photoplethysmogram signal collected by the PPG sensor.

[0034] Step S20: When the signal quality state is stable, obtaining the user's vital sign parameters according to the first photoelectric detection signal.

[0035] The vital sign parameter may be heart rate, blood pressure, or blood oxygen saturation, among others. Calculating the vital sign parameter using the first photodetection signal in a stable state can improve the accuracy of the calculation of the vital sign parameter. For example, when the vital sign parameter is heart rate, the first photodetection signal is a photoplethysmography signal corresponding to a green light source, or a preprocessed and filtered version of the photoplethysmography signal.

[0036] Step S30: When the signal quality state is an uncertain state, obtaining the user's vital sign parameters according to the first photoelectric detection signal and / or the user's first motion data, wherein the first motion data at least includes the user's motion type.

[0037] Among them, the first photoelectric detection signal is in an uncertain state and needs to be analyzed based on the first motion data. According to the analysis result, the first photoelectric detection signal or the first photoelectric detection signal and the first motion data or the first motion data are used to calculate the vital sign parameters.

[0038] In this embodiment, different vital sign signal acquisition methods are adapted according to the signal quality status. When the signal quality of the first photoelectric detection signal is poor, the first motion data is introduced to participate in the calculation of the vital sign parameters, which is beneficial to improving the accuracy of vital sign parameter detection.

[0039] As an implementation manner, step S10 specifically includes the following steps:

[0040] Step S11: performing time-frequency conversion on the first photoelectric detection signal to obtain corresponding photoelectric detection frequency domain data.

[0041] The first photodetection signal may be time-frequency converted by fast Fourier transform to convert the first photodetection signal as time domain data into photodetection frequency domain data. The photodetection frequency domain data may be a spectrum of the first photodetection signal obtained after the time-frequency conversion.

[0042] Step S12: obtaining at least one target peak value from the photoelectric detection frequency domain data, and obtaining quality data of the first photoelectric detection signal according to the target peak value and the total value of the spectrum of the photoelectric detection frequency domain data.

[0043] When there is only one target peak, the target peak can be the maximum amplitude value in the photoelectric detection frequency domain data. When there are multiple target peaks, all amplitude values ​​in the photoelectric detection frequency domain data can be arranged from largest to smallest. The multiple target peaks can be the first N amplitude values, where N is the number of target peaks. Peaks with higher amplitude values ​​are more likely to correspond to the vital sign parameter. Therefore, the peak that can represent the vital sign parameter is selected from the peak corresponding to the target peak.

[0044] The total spectrum value can represent the overall intensity of the photoelectrically detected frequency domain data. The closer the target peak is to the total spectrum value, the greater its impact on the total spectrum value. In other words, the greater the impact of the peak corresponding to the vital sign parameter on the total spectrum value, the better the quality of the first photoelectric detection signal. For example, the quality data can be the ratio of the target peak value to the total spectrum value.

[0045] When the number of target peaks is one, the number of quality data corresponding to the current sampling point is one; when the number of target peaks is multiple, the number of quality data corresponding to the current sampling point is multiple, where each sampling point corresponds to a signal segment of a fixed length in the first photoelectric detection signal.

[0046] For example, the target peak value and the total spectrum value can be obtained within the valid range of the vital sign parameters, such as Figure 3 As shown, when the vital sign parameter is heart rate, its effective range is 40bpm to 240bpm, the target peak value can be one or more peaks with the highest amplitude value within 40bpm to 240bpm, and the total spectrum value can be sum(fft40~240).

[0047] Step S13: determining the signal quality status of the first photodetection signal according to the quality data.

[0048] Among them, the signal quality state of the first photoelectric detection signal can be determined based on the change of the quality data of the first photoelectric detection signal. For example, when the number of target peaks is one, the number of quality data corresponding to each sampling point is one, and the quality data of each sampling point can be compared with the preset quality data threshold. If the quality data is greater than or equal to the preset quality data threshold, the quality data is qualified quality data; if the quality data is less than the preset quality data threshold, the quality data is unqualified quality data. If the first preset number of consecutive quality data are all qualified quality data, the first photoelectric detection signal is determined to be in a stable state; if the second preset number of consecutive quality data are all unqualified quality data, the first photoelectric detection signal is determined to be in an uncertain state. The preset quality data threshold is determined based on an empirical value. Those skilled in the art should understand that in addition to the above examples, other methods can also be used to determine the signal quality state of the first photoelectric detection signal.

[0049] As an implementation manner, the first motion data is obtained based on the motion signal. The signal processing method of this embodiment further includes the following steps:

[0050] Step S10a: obtaining the user's exercise type according to the exercise signal.

[0051] The motion signal may include at least one of an acceleration signal, an angular velocity signal, and an ambient light signal. The acceleration signal can be acquired by a three-axis accelerometer, which detects changes in the acceleration of an object in three perpendicular directions and can capture subtle movements during motion. The angular velocity signal can be acquired by a gyroscope, which detects the rotational speed and angle of an object around its axis and helps determine its direction and posture. The ambient light signal can be acquired by an ambient light sensor, which monitors changes in ambient light to help distinguish between indoor and outdoor activities and activities at different time periods.

[0052] In particular, multiple motion signal features can be extracted from the motion signal, motion feature data can be constructed based on the multiple motion signal features, and the motion feature data can be input into a pre-established motion type recognition model to obtain the motion type output by the motion type recognition model. For example, the motion feature can be a time series feature such as mean, standard deviation, skewness, kurtosis, median, maximum value or minimum value. Motion signal segments corresponding to the preset time period can be obtained from the motion signal at intervals of a preset time period; the mean, standard deviation, skewness, kurtosis, median, maximum value or minimum value of each motion signal segment can be obtained respectively; time series features of the same type are arranged in chronological order to obtain a corresponding feature sequence; motion feature data can be constructed based on the mean feature sequence, standard deviation feature sequence, skewness feature sequence, kurtosis feature sequence, median feature sequence, maximum feature sequence and minimum feature sequence. The motion type recognition model is obtained by training with historical motion feature data. The motion type recognition model can adopt a random forest model, a logistic regression model, etc.

[0053] Exemplarily, the types of exercise may include medium-to-high intensity exercise, and low-intensity exercise; exemplary, the types of exercise may also include running, cycling, swimming, outdoor walking, indoor walking, stair climbing, Pilates, jumping jacks, aerobics, dumbbell lifting, etc.

[0054] During moderate to high intensity exercise (e.g., sprinting, jumping jacks) or some irregular exercise (e.g., walking), a significant increase in vital sign parameters is likely to occur. At this time, the frequency of the first photoelectric detection signal used to characterize the vital sign parameters overlaps with the frequency of the motion signal. Figure 4 As shown in the figure, spectral overlap occurs in the PPG signal time-frequency diagram and the combined acceleration signal time-frequency diagram. When the identified motion type is spectral aliasing, the first photoelectric detection signal is in an uncertain state. It is very likely that spectral aliasing has caused strong interference from the motion signal on the first photoelectric detection signal. A corresponding processing strategy is required to process the first photoelectric detection signal to ensure the accuracy of the vital sign parameter calculation.

[0055] Accordingly, step S20 specifically includes the following steps:

[0056] Step S21: When the signal quality state is uncertain, if the motion type is spectrum aliasing motion, the first photoelectric detection signal is processed according to the processing strategy corresponding to the spectrum aliasing motion, and the user's vital sign parameters are obtained according to the processed first photoelectric detection signal.

[0057] The corresponding processing strategy may be a preset filtering algorithm, and the first photoelectric detection signal is filtered according to the preset filtering algorithm to remove motion artifacts in the first photoelectric detection signal.

[0058] Exemplarily, to address spectral aliasing motion, other photodetection signals are used as reference signals, or other photodetection signals and the motion signal are used together as reference signals for filtering. For example, a second photodetection signal is used as the reference signal; the second photodetection signal is acquired synchronously with the first photodetection signal and uses a different measurement light source type; an adaptive filtering algorithm is used to obtain the motion artifact signal based on the second photodetection signal and the first photodetection signal; and a filtered first photodetection signal is obtained based on the first photodetection signal and the motion artifact signal. For example, the motion signal is used as the first reference signal, and the second photodetection signal is used as the second reference signal; an adaptive filtering algorithm is used to obtain the first motion artifact signal based on the motion signal and the first photodetection signal; an adaptive filtering algorithm is used to obtain the second motion artifact signal based on the second photodetection signal and the first photodetection signal; and a filtered first photodetection signal is obtained based on the first photodetection signal, the first motion artifact signal, and the second motion artifact signal.

[0059] In some embodiments, the first exercise data further includes at least one of resting heart rate, exercise intensity, and exercise frequency.

[0060] Among them, the resting heart rate is the heart rate in a static state. The signal quality is the best at this time, and individual differences are fully taken into account. The resting heart rate can be determined through the user's historical heart rate data; exercise intensity is measured by the three-axis combined acceleration, and can also be divided into different levels such as low, medium, and high according to quantitative standards; exercise frequency can be extracted from the acceleration signal.

[0061] Accordingly, step S20 specifically includes the following steps:

[0062] Step S22: When the signal quality state is an uncertain state, if the motion type is different from the motion type at the last detection moment, obtaining the user's vital sign parameters according to the first motion data.

[0063] The current motion type is different from the motion type at the last detection moment. The user may be in the process of switching motion types. At this time, the first photoelectric detection signal is weak. The vital sign parameters can be calculated based on the first motion data to improve the calculation accuracy of the vital sign parameters. For example, please refer to Figure 5 As shown, during the transition of motion types, such as from rest to sprinting, the spectrum of the first photoelectric detection signal is relatively weak.

[0064] A physiological calculation model of vital sign parameters can be established, and physiological identification features can be constructed using the first exercise data. For example, physiological identification feature data can be constructed based on resting heart rate, exercise intensity, exercise type, and exercise frequency. The physiological identification feature data is input into a pre-established physiological calculation model to obtain the calculated vital sign parameter value output by the physiological calculation model. The physiological calculation model is trained using historical physiological identification feature data. The physiological calculation model can adopt a nonlinear fitting model, a logistic regression model, etc. For example, the calculated vital sign parameter value hr = f(resting heart rate, exercise intensity, exercise type, exercise frequency).

[0065] As an implementation manner, the signal processing method of this embodiment further includes:

[0066] Step S41: when the signal quality state is a transition state, obtaining a first vital sign parameter value according to the first photoelectric detection signal.

[0067] Among them, the transition state can be between the stable state and the uncertain state. The quality of the first photoelectric detection signal begins to decline from the stable state but has not yet reached the uncertain state, or the quality begins to recover from the uncertain state but has not yet reached the stable state. The first photoelectric detection signal is in a transition state.

[0068] The first photoelectric detection signal may be time-frequency converted to obtain corresponding photoelectric detection frequency domain data; and the frequency corresponding to the point with the largest amplitude value in the photoelectric detection frequency domain data is selected as the first vital sign parameter value.

[0069] Step S42: Compare the first vital sign parameter value with the vital sign parameter at the last detection moment to obtain a first comparison result.

[0070] Exemplarily, the first comparison result can be obtained by obtaining the absolute value of the difference between the first vital sign parameter value and the vital sign parameter at the previous detection time, and the ratio of the absolute value of the difference to the vital sign parameter at the previous detection time is the first comparison result. If the first comparison result is less than or equal to a comparison threshold, it indicates that the difference between the first vital sign parameter value and the vital sign parameter at the previous detection time is small; if the first comparison result is greater than the comparison threshold, it indicates that the difference between the first vital sign parameter value and the vital sign parameter at the previous detection time is large; the comparison threshold can be determined based on experience.

[0071] Those skilled in the art should understand that the above method of determining the first comparison result is only an example, and the first comparison result may also be determined in other ways.

[0072] Step S43: Obtaining the user's vital sign parameters according to the calculation strategy corresponding to the first comparison result.

[0073] The first comparison result indicates the degree of difference between the first vital sign parameter value and the vital sign parameter at the last detection moment, and a corresponding calculation strategy can be configured according to the degree of difference to increase the calculation accuracy of the vital sign parameter.

[0074] In some implementations, step S43 specifically includes the following steps:

[0075] Step S431: When the first comparison result is that the difference between the first vital sign parameter value and the vital sign parameter at the previous detection moment is small, the user's vital sign parameter is obtained according to the first vital sign parameter value and the vital sign parameter at the previous detection moment.

[0076] Among them, the difference between the first vital sign parameter value and the vital sign parameter at the last detection moment is small, and the first vital sign parameter value has a greater reference value. The user's vital sign parameters can be obtained based on the first vital sign parameter value and the vital sign parameters at the last detection moment. Exemplarily, a first weight can be configured for the first vital sign parameter value, and a second weight can be configured for the vital sign parameter at the last detection moment. The product of the first vital sign parameter value and the first weight is the first product, and the product of the vital sign parameter at the last detection moment and the second weight is the second product. The user's vital sign parameter is the sum of the first product and the second product, and the sum of the first weight and the second weight is 1.

[0077] In some implementations, the signal processing method of this embodiment further includes the following steps:

[0078] Step S10b: obtaining a second vital sign parameter value of the user according to the first motion data.

[0079] The calculation method of the second vital sign parameter value is specifically referred to the method of calculating the vital sign parameter in step S22, and will not be described in detail here.

[0080] Step S43 specifically includes the following steps:

[0081] Step S432: When the first comparison result is that the first vital sign parameter value is significantly different from the vital sign parameter at the previous detection moment, search for historical state feature data that matches the current state feature data, wherein the current state feature data includes the frequency domain characteristics of the first photoelectric detection signal, the current motion intensity, and the vital sign parameters at the previous detection moment.

[0082] Among them, the first vital sign parameter value is significantly different from the vital sign parameter at the previous detection moment, and the first vital sign parameter value has a relatively small reference value. The current state feature data can be constructed based on the frequency domain characteristics of the first photoelectric detection signal, the current motion intensity, and the vital sign parameters at the previous detection moment. The frequency domain characteristics of the first photoelectric detection signal can be the target peak mentioned in step S12; the historical state feature data can include the frequency domain characteristics of the next historical photoelectric detection signal, the next historical motion intensity, and the vital sign parameters at the previous historical detection moment, and the current state feature data is searched in multiple historical state feature data to obtain historical state feature data that matches the current state feature data.

[0083] Step S433: If historical state feature data matching the current state feature data is found, the user's physical sign parameters are obtained based on the first physical sign parameter value and the physical sign parameters at the last detection moment.

[0084] If matching data is found, it indicates that the signal quality state of the first photoelectric detection signal has similar conditions in historical photoelectric detection signals, and the first vital sign parameter value and the vital sign parameter at the previous detection moment can be directly calculated.

[0085] Step S434: If no historical state feature data matching the current state feature data is found, the user's physical sign parameters are obtained according to the current exercise type, the second physical sign parameter value, and the physical sign parameters at the last detection moment.

[0086] Among them, if no matching data is found, it indicates that the signal quality status of the first photoelectric detection signal has not had a similar situation in the historical photoelectric detection signal, and the first vital sign parameter value and the vital sign parameter at the previous detection moment cannot be used to directly calculate, and the second vital sign parameter value needs to be introduced for calculation.

[0087] Among them, different weights can be configured for the second vital sign parameter value and the vital sign parameter at the last detection moment according to the current exercise type. For example, a third weight can be configured for the second vital sign parameter value, and a fourth weight can be configured for the vital sign parameter at the last detection moment. The product of the second vital sign parameter value and the third weight is the third product, and the product of the vital sign parameter at the last detection moment and the fourth weight is the fourth product. The user's vital sign parameter is the sum of the third product and the fourth product, and the sum of the third weight and the fourth weight is 1.

[0088] Among them, the configuration ratio of the third weight and the fourth weight is different for different sports types.

[0089] In some embodiments, the transition state includes a warning state. When the quality of the first photodetection signal starts to degrade from a stable state but has not yet reached an uncertain state, the first photodetection signal is in the warning state.

[0090] Step S10 specifically includes the following steps:

[0091] Step S101 : obtaining a first photodetection signal segment corresponding to a preset time from a first photodetection signal at every preset time.

[0092] Each first photoelectric detection signal segment is a sampling object of a sampling point, and the end time of the first photoelectric detection signal segment can be used as the detection time of the sampling point. The current first photoelectric detection signal segment is the sampling object of the current detection time.

[0093] Step S102: In the stable state, if the current first photoelectric detection signal segment does not meet the preset quality condition, the stable state is switched to the warning state.

[0094] Among them, the quality data of the first photoelectric detection signal segment is the quality data of the corresponding sampling point. The quality data of the first photoelectric detection signal segment can be obtained through steps S11 and S12, and the first photoelectric detection signal segment is time-frequency converted to obtain the corresponding photoelectric detection segment frequency domain data; at least one target peak is obtained from the photoelectric detection segment frequency domain data, and the quality data of the first photoelectric detection signal segment is obtained based on the target peak and the total value of the spectrum of the photoelectric detection segment frequency domain data.

[0095] Among them, when the quality data of the first photoelectric detection signal segment is greater than or equal to the preset quality data threshold, the current first photoelectric detection signal segment meets the preset quality conditions; when the quality data of the first photoelectric detection signal segment is less than the preset quality data threshold, the current first photoelectric detection signal segment does not meet the preset quality conditions; as long as the quality data of one first photoelectric detection signal segment does not meet the preset quality conditions, the stable state will be switched to the warning state.

[0096] In some embodiments, step S102 further includes the following steps:

[0097] Step S103: In the warning state, when a first number of consecutive first photoelectric detection signal segments do not meet the preset quality condition, the warning state is switched to an uncertain state.

[0098] Wherein, a first number of consecutive sampling points do not meet the preset quality condition, indicating that the first photoelectric detection signal is unstable or the quality cannot be determined, and enters an uncertain state.

[0099] In some embodiments, the transition state further includes a recovery state, and the first photodetection signal recovers in quality from an uncertain state but has not yet reached a stable state, and the first photodetection signal is in the recovery state.

[0100] After step S103, the following steps are also included:

[0101] Step S104 : in the uncertain state, when a second number of consecutive first photoelectric detection signal segments all meet a preset quality condition, switching the uncertain state to a recovery state.

[0102] The second number of consecutive sampling points meeting the preset quality condition indicates that the quality of the first photodetection signal is recovering.

[0103] In some implementations, step S104 may further include the following steps:

[0104] Step S105 : in the recovery state, when a third number of consecutive first photoelectric detection signals all meet the preset quality condition, switching the recovery state to the stable state.

[0105] On the basis that the second number of consecutive sampling points meet the preset quality condition, a third number of consecutive first photodetection signals continue to meet the preset quality condition, indicating that the quality of the first photodetection signal has been restored.

[0106] In some embodiments, before step S10 , the first photodetection signal may be pre-processed.

[0107] During exercise heart rate acquisition, the signal is often interfered with by various noise sources, including power-frequency noise, environmental noise, breathing noise, and noise generated by exercise itself. These interferences can be roughly divided into three categories: abnormal data (such as sudden changes in values), high- and low-frequency noise, and motion artifacts superimposed on the photoplethysmography (PPG) signal.

[0108] Abnormal data refers to data points that deviate from the normal range or data values ​​that change suddenly. To solve this problem, median filtering or statistical methods can be used to identify and remove these abnormal points, thereby improving data quality.

[0109] For high-frequency and low-frequency noise, considering that the effective frequency band of heart rate is between [0.4, 5] Hz, a bandpass filter with appropriate bandwidth can be designed to filter out interference components beyond this frequency range and retain information useful for heart rate monitoring.

[0110] For motion artifacts, when the PPG signal is distorted by physical activity, it is recommended to use adaptive filtering technology or spectral subtraction algorithm, which can effectively separate non-physiological fluctuations caused by muscle contraction, movement and other factors from the original signal, thereby obtaining more accurate and stable heart rate estimation results.

[0111] An embodiment of the present application provides a chip 300, see Figure 6As shown, the chip 300 includes a storage module 31 and a processing module 32, wherein the storage module 31 is used to store the signal processing program; the processing module 32 is used to implement the steps of the above-mentioned signal processing method when executing the signal processing program stored in the storage module.

[0112] Among them, the chip (Integrated Circuit, IC) is also called a chip, and the chip can be but is not limited to a SOC (System on Chip) chip or a SIP (system in package) chip.

[0113] The chip of this embodiment adapts to different vital sign signal acquisition methods according to the signal quality status. When the signal quality of the first photoelectric detection signal is poor, the first motion data is introduced to participate in the calculation of the vital sign parameters, which is beneficial to improving the accuracy of vital sign parameter detection.

[0114] The present application also provides an electronic device 400. Figure 7 As shown, the electronic device 400 includes a device body and a chip 300 as described above, which is provided in the device body. The electronic device can be, but is not limited to, a weight scale, a body fat scale, a nutrition scale, a pulse oximeter, a body composition analyzer, a display, a USB (Universal Serial Bus) docking station, a car, a smart wearable device, a mobile terminal, and a smart home device. Smart wearable devices include, but are not limited to, smart watches, smart bracelets, and cervical massagers. Mobile terminals include, but are not limited to, smart phones, laptops, tablet computers, and POS (point of sales terminals). Smart home devices include, but are not limited to, smart sockets, smart rice cookers, smart sweepers, and smart lights.

[0115] The electronic device of this embodiment adapts different vital sign signal acquisition methods according to the signal quality status. When the signal quality of the first photoelectric detection signal is poor, the first motion data is introduced to participate in the calculation of the vital sign parameters, which is beneficial to improving the accuracy of the vital sign parameter detection.

[0116] The above is only an implementation method of the present application. It should be pointed out that for ordinary technicians in this field, improvements can be made without departing from the creative concept of the present application, but these all fall within the scope of protection of the present application.

Claims

1. A signal processing method, characterized in that: include: acquiring a signal quality status according to the first photoelectric detection signal; When the signal quality state is a stable state, obtaining a user's vital sign parameter according to the first photoelectric detection signal; When the signal quality state is an uncertain state, the user's vital sign parameters are obtained according to the first photoelectric detection signal and / or the user's first motion data, wherein the first motion data at least includes the user's motion type.

2. The signal processing method according to claim 1, wherein: The obtaining of the signal quality status according to the first photoelectric detection signal includes: Performing time-frequency conversion on the first photoelectric detection signal to obtain corresponding photoelectric detection frequency domain data; Obtaining at least one target peak value from the photoelectric detection frequency domain data, and obtaining quality data of the first photoelectric detection signal according to the target peak value and the total value of the spectrum of the photoelectric detection frequency domain data; A signal quality status of the first photodetection signal is determined according to the quality data.

3. The signal processing method according to claim 1, wherein: When the signal quality state is in an uncertain state, obtaining the user's vital sign parameters according to the first photoelectric detection signal and / or the user's first motion data includes: When the signal quality state is an uncertain state, if the motion type is spectrum aliasing motion, the first photoelectric detection signal is processed according to a processing strategy corresponding to the spectrum aliasing motion, and the user's vital sign parameters are obtained according to the processed first photoelectric detection signal.

4. The signal processing method according to claim 1, wherein: The first exercise data further includes at least one of resting heart rate, exercise intensity and exercise frequency; When the signal quality state is an uncertain state, obtaining the user's vital sign parameters according to the first photoelectric detection signal and / or the user's first motion data further includes: When the signal quality state is an uncertain state, if the motion type is different from the motion type at the last detection moment, the user's vital sign parameters are obtained according to the first motion data.

5. The signal processing method according to claim 1, wherein: The signal processing method further includes: When the signal quality state is a transition state, obtaining a first vital sign parameter value according to the first photoelectric detection signal; Comparing the first vital sign parameter value with the vital sign parameter at the last detection moment to obtain a first comparison result; The user's vital sign parameters are obtained according to a calculation strategy corresponding to the first comparison result. The signal processing method according to claim 5 , wherein: The obtaining of the user's vital sign parameters according to the calculation strategy corresponding to the first comparison result includes: When the first comparison result is that the difference between the first vital sign parameter value and the vital sign parameter at the last detection moment is small, the user's vital sign parameter is obtained according to the first vital sign parameter value and the vital sign parameter at the last detection moment.

7. The signal processing method according to claim 5, characterized in that: The signal processing method further includes: obtaining a second vital sign parameter value of the user according to the first motion data; The obtaining of the user's vital sign parameters according to the calculation strategy corresponding to the first comparison result includes: When the first comparison result shows that the first vital sign parameter value is significantly different from the vital sign parameter at the previous detection moment, searching for historical state feature data that matches the current state feature data, wherein the current state feature data includes the frequency domain characteristics of the first photoelectric detection signal, the current motion intensity, and the vital sign parameter at the previous detection moment; If historical state feature data matching the current state feature data is found, obtaining the user's vital sign parameters based on the first vital sign parameter value and the vital sign parameters at the last detection moment; If no historical state feature data matching the current state feature data is found, the user's physical sign parameters are obtained according to the current exercise type, the second physical sign parameter value, and the physical sign parameters at the last detection moment.

8. The signal processing method according to claim 5, wherein: The transition state includes a warning state; The obtaining of the signal quality status according to the first photoelectric detection signal includes: acquiring a first photodetection signal segment corresponding to a preset time from the first photodetection signal at every preset time; In the stable state, if the current first photoelectric detection signal segment does not meet the preset quality condition, the stable state is switched to the warning state.

9. The signal processing method according to claim 8, characterized in that: The obtaining of the signal quality status according to the first photoelectric detection signal further includes: In the warning state, when a first number of consecutive first photoelectric detection signal segments do not meet a preset quality condition, the warning state is switched to an uncertain state.

10. The signal processing method according to claim 8 or 9, characterized in that: The transition state also includes a recovery state; The obtaining of the signal quality status according to the first photoelectric detection signal further includes: In the uncertain state, when a second number of consecutive first photodetection signal segments meet a preset quality condition, the uncertain state is switched to the recovery state.

11. The signal processing method according to claim 10, wherein: The obtaining of the signal quality status according to the first photoelectric detection signal further includes: In the recovery state, when a third number of consecutive first photodetection signals meet a preset quality condition, the recovery state is switched to the stable state.

12. A chip, characterized in that: include: A storage module for storing a signal processing program; The processing module is configured to implement the steps of the signal processing method according to any one of claims 1 to 11 when executing the signal processing program stored in the storage module.

13. An electronic device, characterized in that: Comprising the chip as claimed in claim 12.

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