WO2020187266A1 - 可穿戴设备、信号处理方法及装置 - Google Patents

可穿戴设备、信号处理方法及装置 Download PDF

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Publication number
WO2020187266A1
WO2020187266A1 PCT/CN2020/080018 CN2020080018W WO2020187266A1 WO 2020187266 A1 WO2020187266 A1 WO 2020187266A1 CN 2020080018 W CN2020080018 W CN 2020080018W WO 2020187266 A1 WO2020187266 A1 WO 2020187266A1
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Prior art keywords
signal
collected
wearing user
body movement
motion
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PCT/CN2020/080018
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English (en)
French (fr)
Inventor
汪孔桥
赵明喜
冯镝
赵威
朱国康
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安徽华米信息科技有限公司
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Priority to KR1020207022442A priority Critical patent/KR102438100B1/ko
Priority to US16/770,789 priority patent/US20210106257A1/en
Priority to EP20728375.5A priority patent/EP3733057A4/en
Publication of WO2020187266A1 publication Critical patent/WO2020187266A1/zh

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    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
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Definitions

  • This application relates to the field of signal processing technology, and in particular to a signal processing method, a signal processing device and a wearable device.
  • biosensors on wearable devices collect physiological signals of the human body for medical health monitoring and diagnosis.
  • the physiological signals collected by biosensors are usually relatively weak, and are often interfered by various noises, such as motion noise introduced by human movement, sensor and skin contact noise, etc. These interferences will directly cause the degradation of detection performance, and in severe cases, it will Because the signal is completely submerged in noise and cannot be reconstructed, the detection fails. Due to the relatively high requirements for the detection of physiological signals, any small error will bring a negative psychological burden to the user, so it is necessary to ensure the reliability of the physiological signals.
  • the first purpose of this application is to propose a signal processing method to solve the problem of low reliability of physiological signals collected by wearable devices.
  • the second purpose of this application is to provide a signal processing device.
  • the third purpose of this application is to propose a wearable device.
  • an embodiment of the first aspect of the present application proposes a signal processing method, which is applied to a wearable device, the wearable device is provided with a biosensor and a body motion detection device group, and the method includes: Acquire the human physiological signal collected by the biosensor, divide the human physiological signal into multiple signal frames, each signal frame corresponds to a time range; for each signal frame, according to the body movement detection device group in the signal frame The body motion signal collected in the corresponding time range determines whether the wearing user is in the body motion state; if not, the signal frame is stored in the preset buffer.
  • the human physiological signal collected by the biosensor is first acquired, and the human physiological signal is divided into multiple signal frames, each signal frame corresponds to a time range, and then for each signal frame, according to the body
  • the body motion signal collected by the motion detection device group within the time range corresponding to the signal frame determines whether the wearing user is in the body motion state, and if not, the signal frame is stored in the preset buffer.
  • the method uses the body movement signal collected by the body movement detection device group to sense body movement to filter the human body physiological signals collected by the biosensor, which can ensure that the filtered human body physiological signals are all in the resting state of the wearing user
  • the lower signal improves the stability and reliability of the wearable device for medical health monitoring and diagnosis, and reduces the false alarm rate.
  • the body movement detection device group includes an inertial sensor, an electromyography sensor, and a microphone; the body movement signal collected by the body movement detection device group within the time range corresponding to the signal frame is used to determine the wearing user Whether it is in the body movement state includes: determining whether the wearing user has body movement according to the movement signal collected by the inertial sensor within the time range corresponding to the signal frame; if it is determined that there is no body movement, then according to the signal of the EMG sensor The surface EMG signal collected in the time range corresponding to the frame determines whether the wearing user has body movement; if it is determined that there is no body movement, it is based on the measurement between the skin and the wearable device collected by the microphone in the time range corresponding to the signal frame The sound signal determines whether the wearing user has body movement; if it is determined that the wearing user has body movement according to the body movement signal collected by any body movement detection device, it is determined that the wearing user is in the body movement state.
  • determining whether the wearing user is physically moving according to the motion signal collected by the inertial sensor within the time range corresponding to the signal frame includes: acquiring a first low threshold and a first high threshold, the first A low threshold is obtained based on the motion signal of the wearing user in a resting state collected by the inertial sensor, and the first high threshold is obtained based on the motion signal of the wearing user in a physical state collected by the inertial sensor.
  • a low threshold is less than the first high threshold; the amount of activity of the wearing user is determined according to the collected motion signal; if the amount of activity is less than the first low threshold, it is determined that among the m signal frames before the signal frame Whether there is a signal frame with an activity level greater than the first high threshold; if there is a signal frame with an activity level greater than the first high threshold or if the activity level is greater than the first low threshold, it is determined that the wearing user has Body movement.
  • the time range corresponding to the signal frame includes motion signals at multiple moments; determining the amount of activity of the wearing user according to the collected motion signals includes: comparing the motion signals at each moment with the first Differential processing is performed on the motion signals at n moments to obtain a difference value at each moment; the average value of the difference values at the multiple moments is used as the amount of activity of the wearing user.
  • the time range corresponding to the signal frame includes surface EMG signals at multiple moments; the wearing user is determined according to the surface EMG signals collected by the EMG sensor in the time range corresponding to the signal frame Whether there is body movement, including: obtaining a second low threshold and a second high threshold, the second low threshold is obtained according to the surface EMG signal of the wearing user in a resting state collected by the EMG sensor, and the second The high threshold value is obtained based on the surface EMG signal of the wearing user in the body motion state collected by the EMG sensor, the second low threshold value is less than the second high threshold value; it is determined that the surface EMG signal at the multiple moments The maximum surface EMG signal of, and the average surface EMG signal of the surface EMG signals at the multiple moments; if the maximum surface EMG signal is greater than the second high threshold and the average surface EMG signal is greater than the second low Threshold, it is determined that the wearing user has physical activity.
  • an embodiment of the second aspect of the present application proposes a signal processing device, which is applied to a wearable device, the wearable device is provided with a biosensor and a body motion detection device group, and the device includes:
  • the acquisition module is used to acquire the human physiological signal collected by the biosensor, divide the human physiological signal into multiple signal frames, each signal frame corresponds to a time range, and acquire the body movement collected by the body movement detection device group Signal; judging module, for each signal frame, according to the body motion signal collected by the body motion detection device group in the time range corresponding to the signal frame to determine whether the wearing user is in the body motion state; storage module for When the judgment is no, the signal frame is stored in the preset buffer.
  • the human physiological signal collected by the biosensor is acquired through the acquisition module, the human physiological signal is divided into multiple signal frames, each signal frame corresponds to a time range, and the data collected by the body motion detection device group is obtained Body motion signal, and for each signal frame through the judgment module, determine whether the wearing user is in the body motion state according to the body motion signal collected by the body motion detection device group within the time range corresponding to the signal frame, and the storage module determines whether it is no When, the signal frame is stored in the preset buffer.
  • the device senses body movement by using the body movement signals collected by the body movement detection device group to filter the human physiological signals collected by the biosensor, which can ensure that the filtered human physiological signals are all in the resting state of the wearing user
  • the lower signal improves the stability and reliability of the wearable device for medical health monitoring and diagnosis, and reduces the false alarm rate.
  • the signal processing device proposed according to the foregoing embodiment of the present application may also have the following additional technical features:
  • the body motion detection device group includes an inertial sensor, an electromyography sensor, and a microphone; the judgment module is specifically configured to determine the motion collected by the inertial sensor in the time range corresponding to the signal frame The signal determines whether the wearing user has body movement; if it is determined that there is no body movement, it is determined whether the wearing user has body movement according to the surface EMG signal collected by the EMG sensor in the time range corresponding to the signal frame; if it is determined that there is no body movement , Then determine whether the wearing user is physically moving according to the sound signal between the skin and the wearable device collected by the microphone in the time range corresponding to the signal frame; if the wearing user is determined according to the body movement signal collected by any body movement detection device If there is body movement, it is determined that the wearing user is in a body movement state.
  • the judgment module is specifically configured to obtain the first low threshold value in the process of determining whether the wearing user is physically active according to the motion signal collected by the inertial sensor in the time range corresponding to the signal frame And a first high threshold, the first low threshold is obtained according to the motion signal of the wearing user in a resting state collected by the inertial sensor, and the first high threshold is obtained according to the inertial sensor collecting the wearing user’s body movement State motion signal is obtained, the first low threshold is less than the first high threshold; the amount of activity of the wearing user is determined according to the collected motion signal; if the amount of activity is less than the first low threshold, it is determined Whether there is a signal frame with an activity greater than the first high threshold in the m signal frames before the signal frame; if there is a signal frame with an activity greater than the first high threshold or if the activity is greater than the first low Threshold, it is determined that the wearing user has physical activity.
  • the time range corresponding to the signal frame includes motion signals at multiple moments; the judgment module is specifically configured to determine the amount of activity of the wearing user according to the collected motion signals.
  • the motion signal at each moment and the motion signal at the first n-th moment are subjected to difference processing to obtain a difference value at each moment; the average value of the difference values at the multiple moments is used as the amount of activity of the wearing user.
  • the time range corresponding to the signal frame includes surface EMG signals at multiple moments; the judgment module is specifically configured to determine whether the EMG sensor is within the time range corresponding to the signal frame In the process of determining whether the wearing user is physically active from the collected surface EMG signal, the second low threshold and the second high threshold are obtained, and the second low threshold is based on the surface of the wearing user in a resting state collected by the EMG sensor An electromyographic signal is obtained, the second high threshold is obtained according to the surface electromyographic signal of the wearing user in a body movement state collected by the electromyographic sensor, the second low threshold is less than the second high threshold; determining the The maximum surface EMG signal of the surface EMG signals at multiple times, and the average surface EMG signal of the surface EMG signals at the multiple times; if the maximum surface EMG signal is greater than the second high threshold and the If the average surface EMG signal is greater than the second lower threshold, it is determined that the wearing user is physically moving.
  • an embodiment of the third aspect of the present application proposes a wearable device, which includes a readable storage medium and a processor; wherein, the readable storage medium is used to store machine executable instructions; The processor is configured to read the machine executable instructions on the readable storage medium, and execute the instructions to implement the steps of the method described in the first aspect.
  • the human physiological signal collected by the biosensor provided in the wearable device is acquired, the human physiological signal is divided into multiple signal frames, and each signal frame corresponds to a time range, and then for each Signal frame, according to the body motion signal collected by the body motion detection device set of the wearable device in the time range corresponding to the signal frame to determine whether the wearing user is in the body motion state, if not, store the signal frame to the preset In the cache.
  • the body movement signal collected by the body movement detection device group to perceive body movement to filter the human body physiological signals collected by the biosensor, it can be ensured that the filtered human body physiological signals are all in the resting state of the wearer
  • the lower signal improves the stability and reliability of the wearable device for medical health monitoring and diagnosis, and reduces the false alarm rate.
  • Fig. 1 is a PPG signal diagram according to an exemplary embodiment of this application.
  • Fig. 2A is a flowchart of an embodiment of a signal processing method according to an exemplary embodiment of this application;
  • FIG. 2B is a schematic diagram of a signal frame division structure of a human physiological signal according to the embodiment shown in FIG. 2A;
  • FIG. 2C is a schematic diagram of a surface EMG signal according to the embodiment shown in FIG. 2A of this application;
  • Fig. 3 is a hardware structure diagram of a wearable device according to an exemplary embodiment of this application.
  • Fig. 4 is a structural diagram of an embodiment of a signal processing device according to an exemplary embodiment of this application.
  • first, second, third, etc. may be used in this application to describe various information, the information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.
  • first information may also be referred to as second information, and similarly, the second information may also be referred to as first information.
  • word “if” as used herein can be interpreted as "when” or “when” or "in response to determination”.
  • FIG. 1 is a PPG signal diagram according to an exemplary embodiment of the application.
  • the PPG signals in the right half of FIG. 1 are relatively regular and belong to Normal signal, but the PPG signal in the left half is irregular, which is a noise signal (actually caused by the user's exercise). If the PPG signal in the left half of Figure 1 is used for heart rate detection, the detected heart rate value is the same as the user's actual heart rate value The difference is far, leading to a high false alarm rate.
  • this application proposes a signal processing method.
  • the human physiological signal collected by the biosensor provided in the wearable device is obtained, the human physiological signal is divided into multiple signal frames, and each signal frame corresponds to a time Then, for each signal frame, determine whether the wearing user is in the state of body movement based on the body movement signal collected by the body movement detection device set of the wearable device within the time range corresponding to the signal frame.
  • the signal frame is stored in the preset buffer.
  • the body movement signal collected by the body movement detection device group to perceive body movement to filter the human body physiological signals collected by the biosensor, it can be ensured that the filtered human body physiological signals are all in the resting state of the wearer
  • the lower signal improves the stability and reliability of the wearable device for medical health monitoring and diagnosis, and reduces the false alarm rate.
  • the signal processing method can be applied to a wearable device provided with a biosensor and a body motion detection device group.
  • the sensors may include PPG sensors, pressure sensors, ECG sensors, etc.
  • the body motion detection device group may include inertial sensors (such as accelerometers, gyroscopes, etc.), electromyography sensors, microphones, and other body motion detection devices.
  • the signal processing method includes the following steps:
  • Step 201 Obtain the human physiological signal collected by the biosensor, and divide the human physiological signal into multiple signal frames, and each signal frame corresponds to a time range.
  • the physiological signals of the human body collected by the biosensor during a certain preset period of time may be acquired, and then the collected physiological signals of the human body may be divided into multiple signal frames with the preset duration as a division period.
  • the division method may adopt an overlapping division manner, and of course, a non-overlapping division manner may also be adopted, which is not limited in this application.
  • the overlap division manner the overlap ratio between each signal frame can be set according to practical experience.
  • the PPG signal is divided into signal frames by overlapping division to obtain n signal frames, and the overlap ratio between adjacent signal frames is 50%.
  • Step 202 For each signal frame, determine whether the wearing user is in the body motion state according to the body motion signal collected by the body motion detection device group within the time range corresponding to the signal frame, if yes, go to step 203, otherwise, go to step 204 .
  • the body movement detection device group includes an inertial sensor, an electromyography sensor, and a microphone
  • the sound signal between the skin and the wearable device collected within the corresponding time range determines whether the wearing user is physically moving. If it is determined that the wearing user is physically moving according to the body motion signal collected by any body motion detection device, it is determined that the wearing user is physically moving. Dynamic state.
  • the user's body movement is divided into two types: general movement and slight body movement.
  • general movement type it refers to the obvious movement of the user's limbs in space, and the obvious physical movement that can be noticed, such as raising the arm, shaking the arm, etc., through the movement signal collected by inertial sensors (such as accelerometer, gyroscope, etc.)
  • inertial sensors such as accelerometer, gyroscope, etc.
  • micro-motion types it refers to movements that are not easily detectable by the naked eye, such as small wrist movements that cause muscle tension, and device slippage caused by looser wear between the device and the skin
  • the friction of the inertial sensor is limited by the sensitivity of the inertial sensor.
  • the body movement state can be judged by the movement signal collected by the inertial sensor first, and then the surface collected by the electromyographic sensor is used when the judgment is no body movement.
  • the electromyographic signal is used to determine the body motion state.
  • the sound signal collected by the microphone is used to determine the body motion state.
  • it is determined that the user has no body motion through the three body motion detection devices it is determined The wearing user is at rest.
  • other judgment order can also be used, such as the order of electromyography sensor first, inertial sensor second, and microphone.
  • the process of determining whether the wearing user is physically moving according to the motion signal collected by the inertial sensor in the time range corresponding to the signal frame includes: obtaining a first low threshold and a first high threshold, the first low threshold is Obtained according to the motion signal of the wearing user in the resting state collected by the inertial sensor, the first high threshold is obtained according to the motion signal of the wearing user in the physical state collected by the inertial sensor, the first low threshold is less than the first high threshold, and then according to The collected motion signal determines the amount of activity of the wearing user. If the amount of activity is less than the first low threshold, it is determined whether there is a signal frame with an amount of activity greater than the first high threshold in the m signal frames before the signal frame.
  • m In the m signal frames before the current signal frame, there are signal frames whose activity amount is greater than the first high threshold value or if the activity amount is greater than the first low threshold value, it is determined that the wearing user has physical activity. Among them, m can be adjusted accordingly based on experience.
  • the judging condition that the amount of activity of the current signal frame is greater than the first low threshold considers the local characteristics of the current signal frame
  • the judging condition of m signal frames before the current signal frame considers the first m signals of the current signal frame
  • the first low threshold and the first high threshold may be obtained in advance based on historically collected data, so as to facilitate subsequent use.
  • the motion signals are processed in frames to obtain the average value of the amount of exercise as ACT1.
  • ACT1+delta1 can be used as the first low threshold Threl
  • ACT2-delta2 can be used as the first high threshold Thrre2.
  • delta1 and delta2 represent the disturbance value in the resting state and the disturbance value in the body motion state, respectively.
  • Delta1 and delta2 can be obtained by machine learning methods such as decision trees and SVM (Support Vector Machine), of course. It can be obtained based on experience.
  • the motion signal within the time range corresponding to the signal frame collected by the inertial sensor includes motion signals at multiple moments.
  • the process of determining the amount of activity of the wearing user according to the collected motion signal may include: performing differential processing on the motion signal at each time of the current signal frame and the motion signal at the first n time to obtain each The difference value at one moment, and then the average value of the difference values at multiple moments is used as the amount of activity of the wearing user, where n can be adjusted accordingly based on experience.
  • the motion signal at each moment includes acceleration values in three directions.
  • a calculation method for the amount of activity in the current signal frame can be: the acceleration values in the three directions at each moment in the current frame and the acceleration values in the three directions at the nth moment are subjected to differential processing to obtain the difference in the three directions Value, and then determine the average value of the difference value in each direction, and select the largest average value from the average value of the three directions as the wearer’s activity; another way to calculate the activity of the current signal frame can be: determine every The modulus of the acceleration values in the three directions at a moment, and then the modulus of each moment and the modulus of the first n moments are differentially processed, and finally the average of the difference values at multiple moments is used as the activity of the wearing user the amount.
  • the motion signal at each moment includes rotation angular velocity values in three directions.
  • the calculation method of the amount of activity of the current signal frame can be: the rotation angular velocity values in the three directions at each time and the rotation angular velocity values in the three directions at the first n-th time are differentially processed to obtain the difference values in the three directions, and then Determine the average value of the difference values in each direction, and select the largest average value from the average values of the three directions as the amount of activity of the wearing user.
  • the largest average value is selected from the average values of the three directions as the activity amount of the wearing user.
  • the average value of the average values of the three directions may also be used as the activity amount of the wearing user, or the smallest average value from the average values of the three directions may be selected as the activity amount of the wearing user.
  • the process of whether the user is physically active includes: obtaining the second low threshold and the second high threshold.
  • the second low threshold is obtained based on the surface EMG signal of the user wearing the resting state collected by the EMG sensor, and the second high threshold is based on The surface EMG signal collected by the EMG sensor in the body movement state of the wearing user is obtained, and the second low threshold is less than the second high threshold; then the maximum surface EMG signal among the surface EMG signals at the multiple moments is determined, and the multiple The average surface EMG signal of the surface EMG signal at each moment, if the maximum surface EMG signal is greater than the second high threshold and the average surface EMG signal is greater than the second low threshold, it is determined that the wearing user is physically active.
  • the surface EMG signal is a non-stationary weak signal that is caused by the continuous transmission of nerve impulses to the nerve endings by the human central nerve cells, causing the continuous formation of action potentials generated by the muscle fiber membranes on the skin surface. , Can reflect the movement state of the corresponding skeletal muscle.
  • the surface EMG signal can be used to accurately determine the user's body movement, to ensure that the filtered human physiological signals are the signals of the wearing user in the resting state, and the second low threshold and the second high threshold are used to determine Conditions can avoid misjudgments caused by some abnormal interference.
  • the second low threshold and the second high threshold may also be obtained in advance based on historically collected data for subsequent use.
  • the principle of obtaining the second low threshold and the second high threshold may be the same as that of the first low threshold and the first high threshold. No more details.
  • the surface EMG signal collected by the EMG sensor may be filtered to remove low-frequency baseline drift and 50Hz power frequency interference noise, and the filtering may be a Butterworth filter.
  • the principle of determining whether the wearer is physically active based on the sound signal can be compared with the principle of determining whether the wearer is physically active based on the surface EMG signal The same, this application will not elaborate.
  • Step 203 Delete the signal frame.
  • Step 204 Store the signal frame in a preset buffer.
  • the overlap division method is adopted.
  • the certain signal frame when it is determined that a certain signal frame is collected in a resting state, the certain signal frame is directly stored, if not, the signal frame can be directly deleted.
  • the physiological signals of the human body stored in the preset buffer are the physiological signals of the wearing user in the resting state, which can be accurately used for the analysis and detection of various physiological signs, such as heart rate, HRV, Sleep index, atrial fibrillation, blood oxygen, blood pressure, blood sugar, etc.
  • the human physiological signal collected by the biosensor provided in the wearable device when the human physiological signal collected by the biosensor provided in the wearable device is acquired, the human physiological signal is divided into multiple signal frames, and each signal frame corresponds to a time range, and then for each According to the body motion signal collected by the body motion detection device set of the wearable device within the time range corresponding to the signal frame, it is determined whether the wearing user is in the body motion state. If not, the signal frame is stored in the preset Set in the cache.
  • the body movement signal collected by the body movement detection device group to perceive body movement to filter the human body physiological signals collected by the biosensor, it can be ensured that the filtered human body physiological signals are all in the resting state of the wearer
  • the lower signal improves the stability and reliability of the wearable device for medical health monitoring and diagnosis, and reduces the false alarm rate.
  • Fig. 3 is a hardware structure diagram of a wearable device according to an exemplary embodiment of this application.
  • the wearable device includes: a communication interface 301, a processor 302, a machine-readable storage medium 303, and a bus 304;
  • the interface 301, the processor 302, and the machine-readable storage medium 303 communicate with each other through the bus 304.
  • the processor 302 can execute the above-described signal processing method by reading and executing the machine executable instructions corresponding to the control logic of the signal processing method in the machine-readable storage medium 303. For details of the method, refer to the above-mentioned embodiment. No longer tired.
  • the machine-readable storage medium 303 mentioned in this application may be any electronic, magnetic, optical, or other physical storage device, and may contain or store information, such as executable instructions, data, and so on.
  • the machine-readable storage medium may be: volatile memory, non-volatile memory, or similar storage media.
  • the machine-readable storage medium 303 may be RAM (Radom Access Memory, random access memory), flash memory, storage drive (such as hard drive), any type of storage disk (such as optical disk, DVD, etc.), or similar storage Medium, or a combination of them.
  • FIG. 4 is a structural diagram of an embodiment of a signal processing device according to an exemplary embodiment of this application.
  • the signal processing device can be applied to a wearable device, and the wearable device is provided with a biosensor and a body motion detection device Group, the signal processing device includes:
  • the acquisition module 410 is configured to acquire the human physiological signal collected by the biosensor, divide the human physiological signal into a plurality of signal frames, each signal frame corresponds to a time range, and acquire the body movement detection device group collected Motion signal
  • the judging module 420 is configured to, for each signal frame, determine whether the wearing user is in a body movement state according to the body movement signal collected by the body movement detection device group within the time range corresponding to the signal frame;
  • the storage module 430 is configured to store the signal frame in the preset buffer when the judgment is no.
  • the body motion detection device group includes an inertial sensor, an electromyography sensor, and a microphone;
  • the judging module 420 is specifically configured to determine whether the wearing user has body movement according to the motion signal collected by the inertial sensor in the time range corresponding to the signal frame; if it is determined that there is no body movement, then according to the EMG sensor in the The surface EMG signal collected in the time range corresponding to the signal frame determines whether the wearing user has body movement; if it is determined that there is no body movement, it is based on the distance between the skin and the wearable device collected by the microphone in the time range corresponding to the signal frame Determine whether the wearing user is physically moving or not; if it is determined that the wearing user is physically moving according to the body motion signal collected by any body motion detection device, it is determined that the wearing user is in the physical activity state.
  • the determining module 420 is specifically configured to obtain the first low value in the process of determining whether the wearing user is physically active according to the motion signal collected by the inertial sensor within the time range corresponding to the signal frame. Threshold value and a first high threshold value, the first low threshold value is obtained according to the motion signal of the wearing user in a resting state collected by the inertial sensor, and the first high threshold value is obtained according to the inertial sensor Obtain the motion signal in the active state, the first low threshold is less than the first high threshold; determine the amount of activity of the wearing user according to the collected motion signal; if the amount of activity is less than the first low threshold, determine Whether there is a signal frame with an activity greater than the first high threshold in the m signal frames before the signal frame; if there is a signal frame with an activity greater than the first high threshold or if the activity is greater than the first If the threshold is low, it is determined that the wearing user has physical activity.
  • the time range corresponding to the signal frame includes motion signals at multiple moments
  • the judging module 420 is specifically configured to perform difference processing between the motion signal at each moment and the motion signal at the first n-th moment in the process of determining the amount of activity of the wearing user according to the collected motion signal to obtain each moment
  • the difference value of the difference value; the average value of the difference values at the multiple times is used as the amount of activity of the wearing user.
  • the time range corresponding to the signal frame includes surface EMG signals at multiple times
  • the judgment module 420 is specifically configured to obtain the second low threshold and the second high in the process of determining whether the wearing user is physically active according to the surface EMG signal collected by the EMG sensor in the time range corresponding to the signal frame. Threshold, the second low threshold is obtained based on the surface EMG signal of the wearing user in a resting state collected by the EMG sensor, and the second high threshold is obtained based on the body movement of the wearing user collected by the EMG sensor
  • the surface EMG signal of the state is obtained, the second low threshold is less than the second high threshold; the maximum surface EMG signal in the surface EMG signals at the multiple moments is determined, and the surface muscle at the multiple moments
  • the relevant part can refer to the part of the description of the method embodiment.
  • the device embodiments described above are merely illustrative.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units.
  • Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present application. Those of ordinary skill in the art can understand and implement it without creative work.

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Abstract

本申请提供一种可穿戴设备、信号处理方法及装置，所述信号处理方法应用于可穿戴设备，该可穿戴设备设置有生物传感器和体动检测器件组，所述信号处理方法包括：获取生物传感器采集的人体生理信号，将人体生理信号划分多个信号帧，每个信号帧对应一个时间范围；针对每个信号帧，依据体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态；若否，则将该信号帧存储至预设缓存中。通过利用体动检测器件组采集的体动信号感知体动，以对生物传感器采集的人体生理信号加以过滤，可以保证经过过滤后的人体生理信号均是佩戴用户在静息状态下的信号，提升了可穿戴设备进行医疗健康监测和诊断的稳定可靠性，降低了误警率。

Description

可穿戴设备、信号处理方法及装置
相关申请的交叉引用
本申请基于申请号为201910211438.7，申请日为2019年03月20日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及信号处理技术领域，特别涉及一种信号处理方法、一种信号处理装置和一种可穿戴设备。
背景技术
随着智能硬件的发展，通过可穿戴设备(如手环、手表等)上的生物传感器采集人体的生理信号以进行医疗健康监测和诊断越来越广泛。然而，生物传感器采集的生理信号通常比较微弱，常常会受到各种噪声的干扰，如人体运动引入的运动噪声、传感器与皮肤的接触噪声等，这些干扰会直接导致检测性能的下降，严重时会因信号被完全淹没在噪声中而无法重构，导致检测失败。由于生理信号的检测要求比较高，任何微小的错误都会给用户带来负面的心理负担，因此需要确保生理信号的可靠性。
发明内容
本申请旨在至少从一定程度上解决上述技术中的技术问题之一。为此，本申请的第一个目的在于提出一种信号处理方法，以解决目前可穿戴设备所采集的生理信号可靠性低的问题。
本申请的第二个目的在于提出一种信号处理装置。
本申请的第三个目的在于提出一种可穿戴设备。
为实现上述目的，本申请第一方面实施例提出了一种信号处理方法，所述方法应用于可穿戴设备，所述可穿戴设备设置有生物传感器和体动检测器件组，所述方法包括：获取所述生物传感器采集的人体生理信号，将所述人体生理信号划分多个信号帧，每个信号帧对应一个时间范围；针对每个信号帧，依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态；若否，则将该信号帧存储至预设缓存中。
根据本申请实施例的信号处理方法，先获取生物传感器采集的人体生理信号，将人体 生理信号划分多个信号帧，每个信号帧对应一个时间范围，然后针对每个信号帧，依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，若否，则将该信号帧存储至预设缓存中。由此，该方法通过利用体动检测器件组采集的体动信号感知体动，以对生物传感器采集的人体生理信号加以过滤，可以保证经过过滤后的人体生理信号均是佩戴用户在静息状态下的信号，提升了可穿戴设备进行医疗健康监测和诊断的稳定可靠性，降低了误警率。
另外，根据本申请上述实施例提出的信号处理方法还可以具有如下附加的技术特征：
根据本申请的一个实施例，所述体动检测器件组包括惯性传感器、肌电传感器以及麦克风；依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，包括：依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动；若确定无体动，则依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动；若确定无体动，则依据所述麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动；若依据任一体动检测器件采集的体动信号确定佩戴用户有体动，则确定所述佩戴用户处于体动状态。
根据本申请的一个实施例，依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动，包括：获取第一低阈值和第一高阈值，所述第一低阈值是依据所述惯性传感器采集的佩戴用户处于静息状态的运动信号获得，所述第一高阈值是依据所述惯性传感器采集的佩戴用户处于体动状态的运动信号获得，所述第一低阈值小于所述第一高阈值；依据采集的运动信号确定所述佩戴用户的活动量；若所述活动量小于所述第一低阈值，则判断该信号帧之前的m个信号帧中是否存在活动量大于所述第一高阈值的信号帧；若存在活动量大于所述第一高阈值的信号帧或者若所述活动量大于所述第一低阈值，则确定所述佩戴用户有体动。
根据本申请的一个实施例，该信号帧对应的时间范围内包括多个时刻的运动信号；依据采集的运动信号确定所述佩戴用户的活动量，包括：将每一时刻的运动信号与第前n个时刻的运动信号进行差分处理，得到每一时刻的差分值；将所述多个时刻的差分值的平均值作为所述佩戴用户的活动量。
根据本申请的一个实施例，该信号帧对应的时间范围内包括多个时刻的表面肌电信号；依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动，包括：获取第二低阈值和第二高阈值，所述第二低阈值是依据所述肌电传感器采集的佩戴用户处于静息状态的表面肌电信号获得，所述第二高阈值是依据所述肌电传感器采集的佩戴用户处于体动状态的表面肌电信号获得，所述第二低阈值小于所述第二高阈值； 确定所述多个时刻的表面肌电信号中的最大表面肌电信号，以及所述多个时刻的表面肌电信号的平均表面肌电信号；若所述最大表面肌电信号大于第二高阈值且所述平均表面肌电信号大于第二低阈值，则确定所述佩戴用户有体动。
为实现上述目的，本申请第二方面实施例提出了一种信号处理装置，所述装置应用于可穿戴设备，所述可穿戴设备设置有生物传感器和体动检测器件组，所述装置包括：获取模块，用于获取所述生物传感器采集的人体生理信号，将所述人体生理信号划分多个信号帧，每个信号帧对应一个时间范围，以及获取所述体动检测器件组采集的体动信号；判断模块，用于针对每个信号帧，依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态；存储模块，用于在判断为否时，将该信号帧存储至预设缓存中。
根据本申请实施例的信号处理装置，通过获取模块获取生物传感器采集的人体生理信号，将人体生理信号划分多个信号帧，每个信号帧对应一个时间范围，以及获取体动检测器件组采集的体动信号，并通过判断模块针对每个信号帧，依据体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，通过存储模块在判断为否时，将该信号帧存储至预设缓存中。由此，该装置通过利用体动检测器件组采集的体动信号感知体动，以对生物传感器采集的人体生理信号加以过滤，可以保证经过过滤后的人体生理信号均是佩戴用户在静息状态下的信号，提升了可穿戴设备进行医疗健康监测和诊断的稳定可靠性，降低了误警率。
另外，根据本申请上述实施例提出的信号处理装置还可以具有如下附加的技术特征：
根据本申请的一个实施例，所述体动检测器件组包括惯性传感器、肌电传感器以及麦克风；所述判断模块，具体用于依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动；若确定无体动，则依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动；若确定无体动，则依据所述麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动；若依据任一体动检测器件采集的体动信号确定佩戴用户有体动，则确定所述佩戴用户处于体动状态。
根据本申请的一个实施例，所述判断模块，具体用于在依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动过程中，获取第一低阈值和第一高阈值，所述第一低阈值是依据所述惯性传感器采集的佩戴用户处于静息状态的运动信号获得，所述第一高阈值是依据所述惯性传感器采集的佩戴用户处于体动状态的运动信号获得，所述第一低阈值小于所述第一高阈值；依据采集的运动信号确定所述佩戴用户的活动量；若所述活动量小于所述第一低阈值，则判断该信号帧之前的m个信号帧中是否存 在活动量大于所述第一高阈值的信号帧；若存在活动量大于所述第一高阈值的信号帧或者若所述活动量大于所述第一低阈值，则确定所述佩戴用户有体动。
根据本申请的一个实施例，该信号帧对应的时间范围内包括多个时刻的运动信号；所述判断模块，具体用于在依据采集的运动信号确定所述佩戴用户的活动量过程中，将每一时刻的运动信号与第前n个时刻的运动信号进行差分处理，得到每一时刻的差分值；将所述多个时刻的差分值的平均值作为所述佩戴用户的活动量。
根据本申请的一个实施例，该信号帧对应的时间范围内包括多个时刻的表面肌电信号；所述判断模块，具体用于在依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动过程中，获取第二低阈值和第二高阈值，所述第二低阈值是依据所述肌电传感器采集的佩戴用户处于静息状态的表面肌电信号获得，所述第二高阈值是依据所述肌电传感器采集的佩戴用户处于体动状态的表面肌电信号获得，所述第二低阈值小于所述第二高阈值；确定所述多个时刻的表面肌电信号中的最大表面肌电信号，以及所述多个时刻的表面肌电信号的平均表面肌电信号；若所述最大表面肌电信号大于第二高阈值且所述平均表面肌电信号大于第二低阈值，则确定所述佩戴用户有体动。
为实现上述目的，本申请第三方面实施例提出了一种可穿戴设备，所述设备包括可读存储介质和处理器；其中，所述可读存储介质，用于存储机器可执行指令；所述处理器，用于读取所述可读存储介质上的所述机器可执行指令，并执行所述指令以实现上述第一方面所述方法的步骤。
应用本申请实施例，在获取到可穿戴设备设有的生物传感器采集的人体生理信号时，将所述人体生理信号划分为多个信号帧，每个信号帧对应一个时间范围，然后针对每个信号帧，依据可穿戴设备设有的体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，若否，则将该信号帧存储至预设缓存中。
基于上述描述可知，通过利用体动检测器件组采集的体动信号感知体动，以对生物传感器采集的人体生理信号加以过滤，可以保证经过过滤后的人体生理信号均是佩戴用户在静息状态下的信号，提升了可穿戴设备进行医疗健康监测和诊断的稳定可靠性，降低了误警率。
本申请的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请的上述和/或附加的方面和优点从结合下面附图对实施方式的描述中将变得明显和容易理解，其中：
图1为本申请根据一示例性实施例示出的一种PPG信号图；
图2A为本申请根据一示例性实施例示出的一种信号处理方法的实施例流程图；
图2B为本申请根据图2A所示实施例示出的一种人体生理信号的信号帧划分结构示意图；
图2C为本申请根据图2A所示实施例示出的一种表面肌电信号示意图；
图3为本申请根据一示例性实施例示出的一种可穿戴设备的硬件结构图；以及
图4为本申请根据一示例性实施例示出的一种信号处理装置的实施例结构图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本申请的一些方面相一致的装置和方法的例子。
在本申请使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本申请。在本申请和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。还应当理解，本文中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。
应当理解，尽管在本申请可能采用术语第一、第二、第三等来描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如，在不脱离本申请范围的情况下，第一信息也可以被称为第二信息，类似地，第二信息也可以被称为第一信息。取决于语境，如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”。
目前，通过可穿戴设备(如手环、手表等)上的生物传感器采集人体的生理信号以进行医疗健康监测和诊断，例如通过生物传感器采集的PPG(Photo Plethysmo Graphy，光电容积脉搏波)信号或ECG(electrocardiograph，心电图)信号进行心率检测和心血管疾病诊断，图1为本申请根据一示例性实施例示出的一种PPG信号图，图1中的右半部的PPG信号比较有规律，属于正常信号，而左半部的PPG信号没有规律，属于噪声信号(实际由用户运动所致)，如果利用图1左半部的PPG信号进行心率检测，检测得到的心率值与用户的实际心率值相差甚远，导致误警率很高。
然而，从检测角度来说，只要保证佩戴用户在静息状态下进行生理信号采集，即可避免噪声干扰，确保利用可穿戴设备进行生理疾病诊断的可靠性。
基于此，本申请提出一种信号处理方法，在获取到可穿戴设备设有的生物传感器采集 的人体生理信号时，将所述人体生理信号划分为多个信号帧，每个信号帧对应一个时间范围，然后针对每个信号帧，依据可穿戴设备设有的体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，若否，则将该信号帧存储至预设缓存中。
基于上述描述可知，通过利用体动检测器件组采集的体动信号感知体动，以对生物传感器采集的人体生理信号加以过滤，可以保证经过过滤后的人体生理信号均是佩戴用户在静息状态下的信号，提升了可穿戴设备进行医疗健康监测和诊断的稳定可靠性，降低了误警率。
下面以具体实施例对本申请提出的信号处理方法进行详细阐述。
图2A为本申请根据一示例性实施例示出的一种信号处理方法的实施例流程图，所述信号处理方法可以应用在设置有生物传感器和体动检测器件组的可穿戴设备上，该生物传感器可以包括PPG传感器、压力传感器、ECG传感器等，该体动检测器件组可以包括惯性传感器(如加速度计、陀螺仪等)、肌电传感器、麦克风等体动检测器件。
如图2A所示，所述信号处理方法包括如下步骤：
步骤201：获取生物传感器采集的人体生理信号，将该人体生理信号划分多个信号帧，每个信号帧对应一个时间范围。
在一实施例中，可以获取生物传感器在某一预设时间段采集的人体生理信号，然后以预设时长作为一个划分周期，将采集的人体生理信号划分成多个信号帧。
示例性的，划分方式可以采用重叠划分方式，当然也可以采用非重叠划分方式，本申请对此不进行限定，对于重叠划分方式，每个信号帧之间重叠比例可以根据实践经验设置。
如图2B所示的一段PPG信号，采用重叠划分方式对PPG信号进行信号帧划分，得到n个信号帧，并且相邻信号帧之间的重叠比例为50％。
步骤202：针对每个信号帧，依据体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，若是，则执行步骤203，否则，执行步骤204。
在一实施例中，在体动检测器件组包括惯性传感器、肌电传感器以及麦克风的情况下，可以先依据惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动，若确定无体动，则再依据肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动，若确定无体动，则进一步依据麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动，若依据任一体动检测器件采集的体动信号确定佩戴用户有体动，则确定佩戴用户处于体动状态。
需要说明的是，由于用户的体动分为两种类型：大体动和微体动。对于大体动类型，指的是用户肢体在空间上有明显运动，能够明显察觉的身体动作，如抬胳膊、甩动胳膊等， 通过惯性传感器(如加速度计、陀螺仪等)采集的运动信号便可以检测到活动量以用于判决；对于微体动类型，指的是肉眼不易察觉的动作，如微小的腕部运动导致肌肉紧张、由于佩戴较松弛导致的设备侧滑造成设备与皮肤之间的摩擦等，受限于惯性传感器的灵敏度，通过惯性传感器难以检测到活动量，或者由于活动量太小而淹没在噪声中，但通过肌电传感器采集的表面肌电信号(sEMG-surface EMG)便可以检测到肌肉紧张度以用于判决，或者通过贴近皮肤的麦克风采集的声音信号可以侦听到设备与皮肤之间的摩擦噪声以用于判决。
由此可知，通过融合多个体动检测器件检测的体动参数实现体动状态的综合判决，不仅能够可靠检测大体动，而且还可以准确地检测到微体动。
基于上述分析，对于大体动类型，通过惯性传感器很容易实现判决，因此可以先通过惯性传感器采集的运动信号进行体动状态判决，在判决无体动的情况下，再通过肌电传感器采集的表面肌电信号进行体动状态判决，在判决无体动的情况下，进一步通过麦克风采集的声音信号进行体动状态判决，最终，如果通过三种体动检测器件均确定用户无体动时，确定佩戴用户处于静息状态。当然也可以采用其它判决顺序，如先肌电传感器，后惯性传感器，再麦克风的判决顺序。
本领域技术人员可以理解的是，除了上述的惯性传感器、肌电传感器、麦克风，还可以使用其它用于检测体动的体动检测器件，如脑电波传感器。
在一实施例中，针对依据惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动的过程包括：获取第一低阈值和第一高阈值，第一低阈值是依据惯性传感器采集的佩戴用户处于静息状态的运动信号获得，第一高阈值是依据惯性传感器采集的佩戴用户处于体动状态的运动信号获得，第一低阈值小于第一高阈值，然后，依据采集的运动信号确定佩戴用户的活动量，若所述活动量小于第一低阈值，则判断该信号帧之前的m个信号帧中是否存在活动量大于第一高阈值的信号帧，若所述当前信号帧之前的m个信号帧中存在活动量大于第一高阈值的信号帧或者若所述活动量大于第一低阈值，则确定佩戴用户有体动。其中，m可根据经验相应调整。
其中，当前信号帧的活动量大于第一低阈值的判断条件考虑的是当前信号帧的局部特性，而当前信号帧之前的m个信号帧的判断条件考虑的是当前信号帧的前面m个信号帧的全局特性，通过这两个判断条件能够很好的考虑体动的局部特性和全局特性，使得判决结果更加有效、准确。
在一可能的实施方式中，第一低阈值和第一高阈值可以预先依据历史采集的数据获得，以便于后续使用。假设依据历史采集的静息状态下的大量运动信号，对所述运动信号进行分帧处理，获得运动量的平均值为ACT1，依据历史采集的体动状态下的大量运动信号， 对所述运动信号进行分帧处理，获得运动量的平均值为ACT2，可以将ACT1+delta1作为第一低阈值Thre1，将ACT2–delta2作为第一高阈值Thre2。其中，delta1和delta2分别表示静息状态下的扰动值和体动状态下的扰动值，delta1和delta2可以采用决策树、SVM(Support Vector Machine，支持向量机)等机器学习的方法得到，当然也可以根据经验得到。
在一实施例中，对于惯性传感器采集的该信号帧对应的时间范围内的运动信号包括多个时刻的运动信号。基于此，针对依据采集的运动信号确定所述佩戴用户的活动量的过程，可以包括：通过将当前信号帧的每一时刻的运动信号与第前n个时刻的运动信号进行差分处理，得到每一时刻的差分值，然后将多个时刻的差分值的平均值作为佩戴用户的活动量，其中，n可根据经验相应调整。
在一可能的实施方式中，在惯性传感器为加速度传感器时，每一时刻的运动信号包括三个方向的加速度值。一种当前信号帧的活动量计算方式可以是：将当前帧中每一时刻的三个方向的加速度值与第前n个时刻的三个方向的加速度值进行差分处理，得到三个方向的差分值，然后确定每个方向的差分值的平均值，并从三个方向的平均值中选择最大平均值作为佩戴用户的活动量；另一种当前信号帧的活动量计算方式可以是：确定每一时刻的三个方向的加速度值的模值，然后将每一时刻的模值与第前n个时刻的模值进行差分处理，最后将多个时刻的差分值的平均值作为佩戴用户的活动量。
在另一可能的实施方式中，在惯性传感器为陀螺仪传感器时，每一时刻的运动信号包括三个方向的旋转角速度值。当前信号帧的活动量计算方式可以是：将每一时刻的三个方向的旋转角速度值与第前n个时刻的三个方向的旋转角速度值进行差分处理，得到三个方向的差分值，然后确定每个方向的差分值的平均值，并从三个方向的平均值中选择最大平均值作为佩戴用户的活动量。
需要说明的是，为了严格过滤信号帧，所以从三个方向的平均值中选择最大平均值作为佩戴用户的活动量。但本领域技术人员可以理解的是，还可以将三个方向的平均值的均值作为佩戴用户的活动量，或者从三个方向的平均值中选择最小平均值作为佩戴用户的活动量。
在一实施例中，对于当前信号帧对应的时间范围内包括多个时刻的表面肌电信号，基于此，针对依据肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动的过程包括：获取第二低阈值和第二高阈值，第二低阈值是依据肌电传感器采集的佩戴用户处于静息状态的表面肌电信号获得，第二高阈值是依据肌电传感器采集的佩戴用户处于体动状态的表面肌电信号获得，第二低阈值小于第二高阈值；然后确定该多个时刻的表面肌电信号中的最大表面肌电信号，以及该多个时刻的表面肌电信号的平均表 面肌电信号，若最大表面肌电信号大于第二高阈值且平均表面肌电信号大于第二低阈值，则确定佩戴用户有体动。
其中，表面肌电信号是由人体中枢神经细胞连续传递神经冲动到神经末梢，引起肌肉纤维膜产生的动作电位连续形成的一个个动作电位序列在皮肤表面叠加而成的一种非平稳的微弱信号，能够反映相应骨骼肌的运动状态。如图2C所示的表面肌电信号，用户在静息状态下，表面肌电信号由一些微弱动作形成的微弱噪声构成，而在手掌、手指有较大动作时，表面肌电信号出现明显的波动。由此可知，利用表面肌电信号可以精准判断用户的体动情况，确保过滤后的人体生理信号均是佩戴用户在静息状态下的信号，并且利用第二低阈值和第二高阈值的判断条件可以避免一些异常干扰导致的误判。
示例性的，第二低阈值和第二高阈值也可以预先依据历史采集的数据获得，以便于后续使用，其获得原理与上述第一低阈值和第一高阈值的获得原理可以相同，在此不再详述。
示例性的，在进行体动判断前，可以先对肌电传感器采集的表面肌电信号进行滤波，以去除掉低频基线漂移以及50Hz工频干扰噪声，滤波可采用巴特沃斯滤波器。
在一实施例中，针对依据麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动的过程，由于声音信号表示的是设备与皮肤之间摩擦产生的噪声大小，表面肌电信号表示的是由于肌肉紧张产生的噪声大小，因此依据声音信号确定佩戴用户是否有体动的原理可以与依据表面肌电信号确定佩戴用户是否有体动原理相同，本申请不再详述。
步骤203：删除该信号帧。
步骤204：将该信号帧存储至预设缓存中。
基于上述步骤201所述的信号帧划分方式，对于采用的是重叠划分方式，在确定某一信号帧是在静息状态下采集的，并进行存储时，需要判断该信号帧的前一信号帧是否是在静息状态下采集的，如果否，则将该信号帧与前一信号帧之间重叠的信号删除，然后再存储该信号帧中剩余的信号到预设缓存中。
另外，对于非重叠划分方式，在确定某一信号帧是在静息状态下采集时，直接存储该某一信号帧，如果否，则直接删除该信号帧即可。
经过上述步骤201至步骤204的过程，预设缓存中存储的人体生理信号均是佩戴用户在静息状态下的生理信号，可以准确用于进行各种生理体征的分析检测，如心率、HRV、睡眠指数、房颤、血氧、血压、血糖等。
在本申请实施例中，在获取到可穿戴设备设有的生物传感器采集的人体生理信号时，将所述人体生理信号划分为多个信号帧，每个信号帧对应一个时间范围，然后针对每个信号帧，依据可穿戴设备设有的体动检测器件组在该信号帧对应的时间范围内采集的体动信 号确定佩戴用户是否处于体动状态，若否，则将该信号帧存储至预设缓存中。
基于上述描述可知，通过利用体动检测器件组采集的体动信号感知体动，以对生物传感器采集的人体生理信号加以过滤，可以保证经过过滤后的人体生理信号均是佩戴用户在静息状态下的信号，提升了可穿戴设备进行医疗健康监测和诊断的稳定可靠性，降低了误警率。
图3为本申请根据一示例性实施例示出的一种可穿戴设备的硬件结构图，该可穿戴设备包括：通信接口301、处理器302、机器可读存储介质303和总线304；其中，通信接口301、处理器302和机器可读存储介质303通过总线304完成相互间的通信。处理器302通过读取并执行机器可读存储介质303中与信号处理方法的控制逻辑对应的机器可执行指令，可执行上文描述的信号处理方法，该方法的具体内容参见上述实施例，此处不再累述。
本申请中提到的机器可读存储介质303可以是任何电子、磁性、光学或其它物理存储装置，可以包含或存储信息，如可执行指令、数据，等等。例如，机器可读存储介质可以是：易失存储器、非易失性存储器或者类似的存储介质。具体地，机器可读存储介质303可以是RAM(Radom Access Memory，随机存取存储器)、闪存、存储驱动器(如硬盘驱动器)、任何类型的存储盘(如光盘、DVD等)，或者类似的存储介质，或者它们的组合。
图4为本申请根据一示例性实施例示出的一种信号处理装置的实施例结构图，所述信号处理装置可以应用于可穿戴设备，所述可穿戴设备设置有生物传感器和体动检测器件组，所述信号处理装置包括：
获取模块410，用于获取所述生物传感器采集的人体生理信号，将所述人体生理信号划分多个信号帧，每个信号帧对应一个时间范围，以及获取所述体动检测器件组采集的体动信号；
判断模块420，用于针对每个信号帧，依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态；
存储模块430，用于在判断为否时，将该信号帧存储至预设缓存中。
在一可选实现方式中，所述体动检测器件组包括惯性传感器、肌电传感器以及麦克风；
所述判断模块420，具体用于依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动；若确定无体动，则依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动；若确定无体动，则依据所述麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动；若依据任一体动检测器件采集的体动信号确定佩戴用户有体动，则确定所述佩戴用户处于体动状态。
在一可选实现方式中，所述判断模块420，具体用于在依据所述惯性传感器在该信号 帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动过程中，获取第一低阈值和第一高阈值，所述第一低阈值是依据所述惯性传感器采集的佩戴用户处于静息状态的运动信号获得，所述第一高阈值是依据所述惯性传感器采集的佩戴用户处于体动状态的运动信号获得，所述第一低阈值小于所述第一高阈值；依据采集的运动信号确定所述佩戴用户的活动量；若所述活动量小于所述第一低阈值，则判断该信号帧之前的m个信号帧中是否存在活动量大于所述第一高阈值的信号帧；若存在活动量大于所述第一高阈值的信号帧或者若所述活动量大于所述第一低阈值，则确定所述佩戴用户有体动。
在一可选实现方式中，该信号帧对应的时间范围内包括多个时刻的运动信号；
所述判断模块420，具体用于在依据采集的运动信号确定所述佩戴用户的活动量过程中，将每一时刻的运动信号与第前n个时刻的运动信号进行差分处理，得到每一时刻的差分值；将所述多个时刻的差分值的平均值作为所述佩戴用户的活动量。
在一可选实现方式中，该信号帧对应的时间范围内包括多个时刻的表面肌电信号；
所述判断模块420，具体用于在依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动过程中，获取第二低阈值和第二高阈值，所述第二低阈值是依据所述肌电传感器采集的佩戴用户处于静息状态的表面肌电信号获得，所述第二高阈值是依据所述肌电传感器采集的佩戴用户处于体动状态的表面肌电信号获得，所述第二低阈值小于所述第二高阈值；确定所述多个时刻的表面肌电信号中的最大表面肌电信号，以及所述多个时刻的表面肌电信号的平均表面肌电信号；若所述最大表面肌电信号大于第二高阈值且所述平均表面肌电信号大于第二低阈值，则确定所述佩戴用户有体动。
上述装置中各个单元的功能和作用的实现过程具体详见上述方法中对应步骤的实现过程，在此不再赘述。
对于装置实施例而言，由于其基本对应于方法实施例，所以相关之处参见方法实施例的部分说明即可。以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本申请方案的目的。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本申请的其它实施方案。本申请旨在涵盖本申请的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本申请的一般性原理并包括本申请未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本申请的真正范围和精神由下面的权利要求指出。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
以上所述仅为本申请的较佳实施例而已，并不用以限制本申请，凡在本申请的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本申请保护的范围之内。

Claims (11)

  1. 一种信号处理方法，所述方法应用于可穿戴设备，其特征在于，所述可穿戴设备设置有生物传感器和体动检测器件组，所述方法包括：
    获取所述生物传感器采集的人体生理信号，将所述人体生理信号划分多个信号帧，每个信号帧对应一个时间范围；
    针对每个信号帧，依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态；
    若否，则将该信号帧存储至预设缓存中。
  2. 根据权利要求1所述的方法，其特征在于，所述体动检测器件组包括惯性传感器、肌电传感器以及麦克风；
    依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态，包括：
    依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动；
    若确定无体动，则依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动；
    若确定无体动，则依据所述麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动；
    若依据任一体动检测器件采集的体动信号确定佩戴用户有体动，则确定所述佩戴用户处于体动状态。
  3. 根据权利要求2所述的方法，其特征在于，依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动，包括：
    获取第一低阈值和第一高阈值，所述第一低阈值是依据所述惯性传感器采集的佩戴用户处于静息状态的运动信号获得，所述第一高阈值是依据所述惯性传感器采集的佩戴用户处于体动状态的运动信号获得，所述第一低阈值小于所述第一高阈值；
    依据采集的运动信号确定所述佩戴用户的活动量；
    若所述活动量小于所述第一低阈值，则判断该信号帧之前的m个信号帧中是否存在活动量大于所述第一高阈值的信号帧；
    若存在活动量大于所述第一高阈值的信号帧或者若所述活动量大于所述第一低阈值，则确定所述佩戴用户有体动。
  4. 根据权利要求3所述的方法，其特征在于，该信号帧对应的时间范围内包括多个时 刻的运动信号；
    依据采集的运动信号确定所述佩戴用户的活动量，包括：
    将每一时刻的运动信号与第前n个时刻的运动信号进行差分处理，得到每一时刻的差分值；
    将所述多个时刻的差分值的平均值作为所述佩戴用户的活动量。
  5. 根据权利要求2所述的方法，其特征在于，该信号帧对应的时间范围内包括多个时刻的表面肌电信号；
    依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动，包括：
    获取第二低阈值和第二高阈值，所述第二低阈值是依据所述肌电传感器采集的佩戴用户处于静息状态的表面肌电信号获得，所述第二高阈值是依据所述肌电传感器采集的佩戴用户处于体动状态的表面肌电信号获得，所述第二低阈值小于所述第二高阈值；
    确定所述多个时刻的表面肌电信号中的最大表面肌电信号，以及所述多个时刻的表面肌电信号的平均表面肌电信号；
    若所述最大表面肌电信号大于第二高阈值且所述平均表面肌电信号大于第二低阈值，则确定所述佩戴用户有体动。
  6. 一种信号处理装置，所述装置应用于可穿戴设备，其特征在于，所述可穿戴设备设置有生物传感器和体动检测器件组，所述装置包括：
    获取模块，用于获取所述生物传感器采集的人体生理信号，将所述人体生理信号划分多个信号帧，每个信号帧对应一个时间范围，以及获取所述体动检测器件组采集的体动信号；
    判断模块，用于针对每个信号帧，依据所述体动检测器件组在该信号帧对应的时间范围内采集的体动信号确定佩戴用户是否处于体动状态；
    存储模块，用于在判断为否时，将该信号帧存储至预设缓存中。
  7. 根据权利要求6所述的装置，其特征在于，所述体动检测器件组包括惯性传感器、肌电传感器以及麦克风；
    所述判断模块，具体用于依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动；若确定无体动，则依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动；若确定无体动，则依据所述麦克风在该信号帧对应的时间范围内采集的皮肤与可穿戴设备之间的声音信号确定佩戴用户是否有体动；若依据任一体动检测器件采集的体动信号确定佩戴用户有体动，则确定所述佩戴用户处于体动状态。
  8. 根据权利要求7所述的装置，其特征在于，所述判断模块，具体用于在依据所述惯性传感器在该信号帧对应的时间范围内采集的运动信号确定佩戴用户是否有体动过程中，获取第一低阈值和第一高阈值，所述第一低阈值是依据所述惯性传感器采集的佩戴用户处于静息状态的运动信号获得，所述第一高阈值是依据所述惯性传感器采集的佩戴用户处于体动状态的运动信号获得，所述第一低阈值小于所述第一高阈值；依据采集的运动信号确定所述佩戴用户的活动量；若所述活动量小于所述第一低阈值，则判断该信号帧之前的m个信号帧中是否存在活动量大于所述第一高阈值的信号帧；若存在活动量大于所述第一高阈值的信号帧或者若所述活动量大于所述第一低阈值，则确定所述佩戴用户有体动。
  9. 根据权利要求8所述的装置，其特征在于，该信号帧对应的时间范围内包括多个时刻的运动信号；
    所述判断模块，具体用于在依据采集的运动信号确定所述佩戴用户的活动量过程中，将每一时刻的运动信号与第前n个时刻的运动信号进行差分处理，得到每一时刻的差分值；将所述多个时刻的差分值的平均值作为所述佩戴用户的活动量。
  10. 根据权利要求7所述的装置，其特征在于，该信号帧对应的时间范围内包括多个时刻的表面肌电信号；
    所述判断模块，具体用于在依据所述肌电传感器在该信号帧对应的时间范围内采集的表面肌电信号确定佩戴用户是否有体动过程中，获取第二低阈值和第二高阈值，所述第二低阈值是依据所述肌电传感器采集的佩戴用户处于静息状态的表面肌电信号获得，所述第二高阈值是依据所述肌电传感器采集的佩戴用户处于体动状态的表面肌电信号获得，所述第二低阈值小于所述第二高阈值；确定所述多个时刻的表面肌电信号中的最大表面肌电信号，以及所述多个时刻的表面肌电信号的平均表面肌电信号；若所述最大表面肌电信号大于第二高阈值且所述平均表面肌电信号大于第二低阈值，则确定所述佩戴用户有体动。
  11. 一种可穿戴设备，其特征在于，所述设备包括可读存储介质和处理器；
    其中，所述可读存储介质，用于存储机器可执行指令；
    所述处理器，用于读取所述可读存储介质上的所述机器可执行指令，并执行所述指令以实现权利要求1-5任一所述方法的步骤。
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