WO2017092018A1 - 一种生物信号采集方法、装置、电子设备及系统 - Google Patents

一种生物信号采集方法、装置、电子设备及系统 Download PDF

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Publication number
WO2017092018A1
WO2017092018A1 PCT/CN2015/096362 CN2015096362W WO2017092018A1 WO 2017092018 A1 WO2017092018 A1 WO 2017092018A1 CN 2015096362 W CN2015096362 W CN 2015096362W WO 2017092018 A1 WO2017092018 A1 WO 2017092018A1
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Prior art keywords
biosignal
user
motion sensor
duration
activity
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PCT/CN2015/096362
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English (en)
French (fr)
Inventor
许培达
陈文娟
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华为技术有限公司
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Priority to CN201580085088.4A priority Critical patent/CN108366742B/zh
Priority to PCT/CN2015/096362 priority patent/WO2017092018A1/zh
Priority to EP23151271.6A priority patent/EP4218555B1/en
Priority to EP15909532.2A priority patent/EP3375357B1/en
Priority to US15/780,696 priority patent/US20180353107A1/en
Publication of WO2017092018A1 publication Critical patent/WO2017092018A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • A61B5/7289—Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0204—Acoustic sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0223—Magnetic field sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0247—Pressure sensors

Definitions

  • Embodiments of the present invention relate to the field of communications technologies, and, more particularly, to a biosignal acquisition method, apparatus, electronic device, and system.
  • the duration of biosignal acquisition is a key indicator.
  • the acquisition duration is too long, which will affect the user experience.
  • the acquisition duration is too short, and the number of signal values that may be acquired is insufficient, resulting in large errors and difficult to guarantee accuracy.
  • wearable devices in the mobile health market use fixed-time methods when collecting biosignals. Most of them are determined by market requirements of products or opinions of technical experts, and subjectivity is strong. Therefore, it is necessary to reasonably determine the collection duration of the biosignal to take into account the user experience and equipment accuracy requirements.
  • the present application provides a biosignal acquisition method, device, electronic device and system to achieve reasonable control of the biosignal acquisition duration and improve the measurement accuracy of the biosignal.
  • an embodiment of the present application provides a biosignal acquisition method, the method comprising: acquiring output data of at least one motion sensor; controlling at least one biosignal of the user according to at least output data of the at least one motion sensor Collecting the duration; collecting the at least one biosignal of the user during the acquisition duration of the at least one biosignal.
  • the user's motion state information controls the user's biosignal acquisition duration to flexibly control the biosignal acquisition duration, thereby improving the measurement accuracy of the biosignal.
  • the motion sensor can include any of an accelerometer, a gyroscope, a pressure sensor, a microphone, a magnetometer, and an altimeter.
  • At least one of a user's activity type and activity intensity may be identified according to at least the output data of the at least one motion sensor; And generating, by the first duration, at least one biosignal of the user according to the first duration that matches at least one of the activity type and the activity intensity of the user.
  • the activity type may include various examples such as running, walking, cycling, swimming, climbing, standing, sitting, sleeping, and the like.
  • any situation that depicts a user's actions and/or movements may be referred to as an "activity.”
  • activity For the same biosignal, it is different under different activity types and different activity intensities, and it will be affected by EMG noise and motion artifacts, depending on the user's current activity type and/or Activity intensity Choosing the appropriate acquisition duration can further improve the accuracy of biosignal measurement.
  • the at least one biosignal has a periodicity, such as an electrocardiogram (ECG), a pulse wave (PPG), or the like.
  • ECG electrocardiogram
  • PPG pulse wave
  • the number of feature reference points of the acquired biosignals can be detected when the number of feature reference points reaches When a certain amount is stopped, the biosignal is stopped to ensure the accuracy of the measurement.
  • At least one of a user's activity type and activity strength may be identified based on at least the output data of the at least one motion sensor; obtaining an activity type with the user Detecting a first value of at least one of the activity strengths, detecting a number of feature reference points of the at least one biosignal, and stopping collecting the at least when the number of the feature reference points is equal to the first value A biological signal.
  • the accuracy of periodic biosignal measurements can be further improved by selecting the appropriate acquisition duration based on the user's current activity type and/or activity intensity.
  • an embodiment of the present application provides a biosignal acquisition device, the collection
  • the device has the function of implementing the method of any of the above first aspect or of the above first aspect.
  • the functions may be implemented by hardware or by corresponding software implemented by hardware.
  • the hardware or software includes one or more modules corresponding to the functions described above.
  • the apparatus includes: an acquisition unit configured to acquire output data of the at least one motion sensor; and a control unit configured to control the at least one biosignal of the user based on at least the output data of the at least one motion sensor Collecting time; collecting unit, configured to collect the at least one biosignal of the user during a collection duration of the at least one biosignal.
  • an embodiment of the present application provides an electronic device, the electronic device having the function of implementing the method in any one of the foregoing first aspect or the first aspect.
  • the functions may be implemented by hardware or by corresponding software implemented by hardware.
  • the hardware or software includes one or more modules corresponding to the functions described above.
  • the electronic device includes: at least one motion sensor for monitoring motion of the user; a memory for storing instructions or data; and a processor coupled to the memory, the processor for implementing the above The function of obtaining the output data of the at least one motion sensor and controlling the acquisition duration of the at least one biosignal of the user according to the output data of the at least one motion sensor; And at least one biosensor for collecting the at least one biosignal of the user during an acquisition duration of the at least one biosignal.
  • an embodiment of the present application provides a biosignal acquisition system, the system having the function of implementing the method in any one of the foregoing first aspect or the first aspect.
  • the system includes: at least one motion sensor for monitoring motion of a user; a memory for storing instructions or data; a processor coupled to the memory, the processor for implementing the first aspect or the first aspect above
  • the at least one motion sensor is coupled to the processor via a wireless interface.
  • the at least one motion sensor is coupled to the processor via a wired interface.
  • the at least one biosensor and the processor pass Wireless interface coupling.
  • the at least one biosensor and the processor pass Wired interface coupling.
  • the at least one motion sensor and the processor are disposed in the same device. , or set separately in different devices.
  • the at least one biosensor and the processor are disposed in the same device. , or set separately in different devices.
  • an embodiment of the present invention provides a computer storage medium, configured to store computer software instructions for use in the foregoing electronic device, including any one of the implementation manners of the first aspect or the foregoing The program designed by the method.
  • the solution provided by the present invention can flexibly control the duration of biosignal acquisition and improve the accuracy of biosignal measurement.
  • FIG. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
  • FIG. 2 is a flowchart of a biosignal acquisition method according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of another biosignal acquisition method according to an embodiment of the present invention.
  • Figure 5 is a schematic diagram of an electrocardiogram signal
  • Figure 6 is a schematic diagram of a pulse wave signal
  • Figure 7 is a schematic diagram of a complete ECG signal waveform
  • Figure 8 is a schematic diagram of pulse arrival time
  • FIG. 9 is a flowchart of still another biosignal acquisition method according to an embodiment of the present invention.
  • FIG. 10 is a schematic structural diagram of a biological signal collection apparatus according to an embodiment of the present invention.
  • FIG. 11 is a schematic structural diagram of still another electronic device according to an embodiment of the present invention.
  • FIG. 12 is a schematic structural diagram of a biological signal acquisition system according to an embodiment of the present invention.
  • FIG. 13 is a schematic diagram of a specific application scenario of a biosignal acquisition system according to another embodiment of the present invention.
  • electronic device 100 can include a processor 101, a bus 104, a memory 108, and a sensor 102.
  • the memory 108 may include one or more storage media including, for example, a hard disk drive, a solid state drive, a flash memory, a persistent storage such as a read only memory (“ROM”), such as a random access memory (“RAM”). Semi-permanent memory, any other suitable type of storage component, or any combination thereof.
  • the memory 108 can be a built-in memory or an external memory.
  • Built-in memory Volatile memory such as (DRAM: dynamic RAM), static random access memory (SRAM: static RAM), synchronous dynamic random access memory (SDRAM), or one-time programmable read-only memory (OTPROM: one time programmable ROM) ), programmable read only memory (PROM: programmable ROM), erasable programmable read only memory (EPROM), electrically erasable and programmable ROM (EEPROM), mask Non-volatile memory (mask ROM), flash ROM, NAND flash memory, NOR flashmemory (non-volatile) At least one of Memory).
  • the built-in memory can also take the form of a solid state drive (SSD: Solid State Drive).
  • External memory can include compact flash (CF: compact flash), secure digital (SD: secure digital), micro secure digital (Micro-SD: micro secure digital), mini secure digital (Mini-SD: mini secure digital), extreme At least one of a digital (xD: extreme digital) or a memory stick.
  • CF compact flash
  • secure digital secure digital
  • micro secure digital micro secure digital
  • mini secure digital mini secure digital
  • At least one of a digital xD: extreme digital
  • xD extreme digital
  • electronic device 100 includes more than one sensor (eg, sensor 102 and sensor 103 in FIG. 1).
  • the sensor 102 is a motion sensor for monitoring the motion of the user, and the sensor 102 may include any one of an accelerometer, a gyroscope, a pressure sensor, a microphone, a magnetometer, and an altimeter, and may also include a brightness sensor, an optical sensor, and proximity.
  • Any of the sensors, any sensor used to monitor the motion of the user may be referred to as a motion sensor, and thus the examples cited are not to be construed as limiting the disclosure.
  • the sensor 103 is a biosensor for monitoring a user's biosignal, and the sensor 103 may include an olfactory sensor (E-nose sensor), an EMG sensor (electromyography sensor), an EEG sensor (electroencephalogram sensor), and an electroencephalogram sensor. Sensor), ECG sensor (electrocardiogram sensor) or fingerprint sensor. And, the sensor 102 and the sensor 103 can measure the physical quantity or sense the operating state of the electronic device, and convert the measured or sensed information into an electrical signal.
  • E-nose sensor E-nose sensor
  • EMG sensor electrochromography sensor
  • EEG sensor electroencephalogram sensor
  • electroencephalogram sensor electroencephalogram sensor
  • Sensor ECG sensor (electrocardiogram sensor) or fingerprint sensor.
  • the sensor 102 and the sensor 103 can measure the physical quantity or sense the operating state of the electronic device, and convert the measured or sensed information into an electrical signal.
  • the electronic device 100 further includes an input module 105 that can receive commands or data from the user and pass it to the processor 101 or memory via the bus 104.
  • the input module 105 can include a touch panel, a key, or an ultrasound input device.
  • the touch panel can recognize the touch input by at least one of a capacitive type, a pressure sensitive type, an infrared type, or an ultrasonic method.
  • the touchpad may also include a controller. For capacitive, not only can the direct touch be recognized, but the proximity can also be identified.
  • the touch panel may also include a tactile layer. At this point, the touchpad can provide a tactile response to the user.
  • the key may include a keyboard or a touch key
  • the ultrasonic input device is a device that can confirm the data by sensing the micro sound wave in the electronic device by the pen that generates the ultrasonic signal, and can be used for wireless recognition.
  • the electronic device 100 further includes a display module 106 that can display graphics, images or data to the user.
  • display module 106 can include a panel.
  • the panel may be an LCD (liquid-crystal display), an LED (light emitting diode display), or an AMOLED (active-matrix organic light-emitting diode).
  • the panel can be constructed to be flexible, transparent, or wearable.
  • the panel can also be configured as a module with the touch panel.
  • display module 106 may also include control circuitry for the control panel.
  • electronic device 100 also includes communication module 107 to enable device 100 to communicate with one or more other electronic devices or servers (not shown) using any suitable communication protocol.
  • the communication module 107 can support a short-range communication protocol such as Wi-Fi (wireless fidelity), Bluetooth (BT: Bluetooth), Near Field Communication (NFC) or, for example, the Internet.
  • Wi-Fi wireless fidelity
  • Bluetooth Bluetooth
  • NFC Near Field Communication
  • the communication module 107 can also include circuitry that enables the electronic device 100 to couple with another device (eg, a computer) to communicate with the other device by wire or wirelessly.
  • the bus 104 may connect components (for example, the processor 101, the memory 108, the sensor 102, the sensor 103, the input module 105, and the display module 106) included in the electronic device 100 to each other, and enable communication between constituent elements. Circuit.
  • the processor 101 is operative to execute instructions (e.g., instructions fetched from the input module 105), interrupt handling, timing, and other functions. Additionally, the processor 101 may further include a graphics processing unit.
  • the memory 108 can store instructions or data received by the processor 101 or other constituent elements (e.g., from the input module 105, the display module 106, the communication module 107) or generated by the processor 101 or other constituent elements.
  • the memory 108 may include an internal buffer and an external buffer.
  • the memory 108 may also include a kernel, a middleware, and an API application programming interface.
  • the kernel may control or manage system resources (e.g., bus 104, processor 101, or memory 108) for performing actions or functions implemented by other program modules (e.g., middleware, APIs, or applications).
  • the kernel can provide an interface for controlling or managing individual components of the electronic device 100 from a middleware, API, or application.
  • Middleware can perform mediation to enable APIs or applications to communicate with the kernel to exchange data.
  • the middleware can prioritize the use of system resources (eg, bus 104, processor 101, or memory 108) of the electronic device 100 for job requests received from more than one application, such that load balancing for job requests can be performed (load Balancing).
  • the API is an interface for controlling functions provided by a kernel or middleware through an application, and may include at least one interface or function for file control, window control, image processing, or text control.
  • FIG. 2 is a flowchart of a method for collecting a biological signal according to an embodiment of the present invention.
  • the method provided in this embodiment may be applied to the electronic device 100 shown in FIG. 1.
  • the electronic device 100 may include, but is not limited to, Wearable devices and other portable and non-portable computing devices, such as smart bracelets, smart watches, smart phones, tablets, and laptops. Please refer to FIG. 2, including the following steps:
  • Step S210 Acquire output data of at least one motion sensor.
  • the motion state information of the user is obtained by at least one motion sensor (eg, sensor 102 in FIG. 1).
  • the motion sensor includes an accelerometer, a gyroscope, and a magnetometer, wherein the accelerometer, the gyroscope, and the magnetometer can measure three axial data changes in three dimensions, forming a A 9-axis attitude detection sensor.
  • motion sensor 102 can be implemented as a microelectromechanical system (MEMS).
  • MEMS microelectromechanical system
  • the output data of the motion sensor is raw data, and in other embodiments, the output data of the motion sensor is processed data, for example, an electronic device calculated by output data of a plurality of motion sensors. Direction of movement and speed of movement.
  • the output data of the at least one motion sensor is acquired according to a preset time period, for example, the motion state information of the user is acquired every 1 to 3 seconds.
  • only the output data of at least one motion sensor within a preset time period (eg, 5 seconds) is acquired for subsequent information processing.
  • Step S220 Control the acquisition duration of the at least one biosignal of the user according to at least the output data of the at least one motion sensor.
  • the electronic device includes at least one biosignal sensor (eg, sensor 103 in FIG. 1) capable of detecting a biosignal of the user, the biosignal comprising an electrocardiogram (ECG), an electroencephalogram (EEG), Electromyography (EMG), bioelectrical impedance, body temperature, blood sugar, blood oxygen, blood pressure, photoplethysmographic pulse wave (PPG), etc.
  • a biosignal sensor eg, sensor 103 in FIG. 1
  • the biosignal comprising an electrocardiogram (ECG), an electroencephalogram (EEG), Electromyography (EMG), bioelectrical impedance, body temperature, blood sugar, blood oxygen, blood pressure, photoplethysmographic pulse wave (PPG), etc.
  • the user actively activates a biosignal sensor with a specific function to start collecting biosignal information, or the electronic device controls the biosensor to periodically detect certain biosignal information automatically.
  • This information can be stored on the device itself or through sharing with other devices or via network communication to the remote device.
  • a user collecting ECG and heart rate data may need to touch several dry sensors with both hands or be able to use a capacitance (eg, non-contact) that allows ECG and heart rate data to be collected by placing the sensor only close to the chest.
  • the sensor or can use an oxymetric sensor to measure heart rate at the fingertip.
  • Step S230 Collect the at least one biosignal of the user within a collection duration of the at least one biosignal.
  • step S220 may specifically include the following steps:
  • Step 301 Identify at least one of a user's activity type and activity intensity based on at least the output data of the at least one motion sensor.
  • Step 302 Acquire a first duration that matches at least one of an activity type and an activity strength of the user.
  • Step 303 Collect at least one biosignal of the user according to the first duration, and stop acquiring the at least one biosignal when the first duration expires.
  • the term "activity” may include various examples such as running, walking, cycling, swimming, climbing, standing, sitting, sleeping, and the like. In general, any description of a user's actions and/or movements may be referred to as "activities" and thus the examples cited are not to be construed as limiting the disclosure.
  • the intensity of the activity selects the appropriate acquisition duration to improve the accuracy of the biosignal measurement.
  • step 301 can determine the type of user activity according to the method shown in FIG. 4, as shown in FIG. 4, including the following steps:
  • Step 401 Perform filtering processing on output data of at least one motion sensor.
  • the output data of the motion sensor typically contains a lot of noise, and the output data of the acquired at least one motion sensor (eg, sensor 102 in FIG. 1) may be filtered or otherwise processed to delete a portion of the data.
  • the vibration-related data is eliminated or reduced by a filtering process in which the vibration is caused by a car, a train, or a ship, for example, when a person is on a car that has started but does not drive, the person is actually at rest.
  • inertial sensors still produce sensory data.
  • Another example is that when a person is talking, there will be slight physical movements, and the inertial sensor will also generate data related to vibration.
  • Step 402 Calculate a feature value according to output data of the at least one motion sensor.
  • data of an inertial sensor such as a gyroscope or an accelerometer is acquired to calculate a set of feature values from each set of data.
  • the feature value includes a signal mean or standard deviation calculated from a signal source.
  • the accelerometer detects the acceleration values (X, Y, Z) in the three directions of X, Y, and Z axes.
  • the eigenvalues in the acquisition period T can be calculated according to the following formula:
  • n is the number of sampling points in the sampling period T
  • i is the sampling point number
  • X(i) is the sampling point signal value, 1 ⁇ i ⁇ n, For the mean.
  • Step 403 Determine the activity type of the user according to the feature value calculated in step 402.
  • the feature value calculated in step 402 may be compared with a threshold to determine the type of activity of the user. For example, if the accelerometer standard deviation is above a certain threshold A, the user is assumed to be running; otherwise, if the accelerometer standard deviation is above the threshold B below the threshold A, then the user is assumed to be walking, otherwise the user is It is assumed that standing or sitting still, according to which the types of activities of the users can be distinguished from each other.
  • the user's activity type or activity intensity may also be identified according to other useful information, wherein other useful information includes navigation information (eg, location, speed information), input device information (eg, microphone captured) Audio data, image information captured by the camera), determined context (eg, determining the context by user's touch screen operation, key operation, sounding, etc. to determine the user's activity type). For example, the speed, the position, and the path of the user are determined based on the output data of the GPS module, and the user's activity type is more accurately determined accordingly.
  • navigation information eg, location, speed information
  • input device information eg, microphone captured
  • Audio data image information captured by the camera
  • determined context eg, determining the context by user's touch screen operation, key operation, sounding, etc. to determine the user's activity type.
  • the speed, the position, and the path of the user are determined based on the output data of the GPS module, and the user's activity type is more accurately determined accordingly.
  • the number of steps per unit time may be determined; and in swimming, the unit time may be determined.
  • the number of strokes The activity intensity is calculated according to the exercise frequency, the amount of movement each time in the repeated exercise, and the weight of the subject. Assuming that the sport is running, then The running speed obtained by multiplying the running frequency and the stride can be used as the activity intensity. As another example, assuming that the exercise is swimming, the product of the swing arm frequency and the swing arm amplitude can be used as the activity intensity.
  • the intensity of the activity can also be characterized by parameters such as respiratory rate or oxygen consumption per unit time, and the cited examples should not be construed as limiting the disclosure.
  • a mapping relationship between the user activity intensity (or activity type) and the biosignal acquisition duration can be established. When the biosignal acquisition duration is needed, the acquisition duration that best matches the current activity intensity (or activity type) is obtained by looking up the table. For the same activity type, it may correspond to multiple activity intensities, and the collection duration will be different under different activity intensities. Take running as an example, construct a mapping relationship table as shown in Table 1, and find and obtain the user. The duration of the acquisition in which the current active state matches.
  • the activity intensity of the user may be divided into levels, for example, two acceleration thresholds A1 and A2 are set, A1 ⁇ A2, and if the acceleration value is less than the threshold A1, the user activity intensity level is low. It is denoted as L; otherwise, if the acceleration value is higher than the threshold A1 and less than the threshold A2, then the user activity intensity level is medium, denoted as M; if the acceleration value is higher than the threshold A2, the user motion level is high, denoted as H. As the user's activity intensity level increases, the noise will also increase, so the required biosignal acquisition time will gradually increase.
  • One type of activity may correspond to multiple activity intensity levels, for example, assuming the exercise is running, the user may jog, run, or run. As another example, if the motion is walking, the user can be a slow step, a middle step, or a quick step.
  • the user's activity type and activity intensity level can be simultaneously determined, thereby obtaining the best match with the user's current activity state.
  • the duration of the signal acquisition Taking running as an example, construct a mapping relationship table as shown in Table 2, and find and obtain the collection duration that matches the current active state of the user.
  • the user's activity type can be divided into two types: static and motion.
  • static When the user is in motion, the user is susceptible to electromyography noise and motion artifacts.
  • the biosignal quality is lower than that in the non-motion state. The quality is poor, and a longer acquisition time can be set than in the stationary state to improve the accuracy of the biosignal measurement.
  • the biosignal can be a signal with periodicity and can include, for example, an electrocardiogram (ECG), a pulse wave (PPG), or other signal with a periodicity.
  • ECG electrocardiogram
  • PPG pulse wave
  • the biosignal may correspond to an ECG waveform as shown in FIG. 5, or a PPG waveform as shown in FIG. 6.
  • the ECG waveform may be a quasi-periodic signal having a repeating pattern of periodic PQRST waveforms, consisting of P waves, QRS waves and T waves (Fig. 7), and U waves (low voltage wavelets, not shown) after T waves.
  • P wave represents the atrial depolarization process
  • QRS complex represents the depolarization process of the ventricle
  • T wave represents the repolarization process of the ventricle.
  • the QRS complex consists of three closely connected waves. The first downwardly deflected wave is called the Q wave. The high-point vertical wave after the Q wave is called the R wave. The R wave is deflected downward. Called S wave, the normal QRS wave group time is 0.06 to 0.10 seconds.
  • the PR interval refers to the time from the start of the P wave to the start of the QRS complex. In general, the adult PR interval is 0.12 to 0.20 seconds. The PR interval varies with heart rate and age. Generally, the older the age, the longer the PR interval.
  • the S-T segment refers to a horizontal line from the end of the QRS complex to the beginning of the T wave.
  • the QT interval is the period from the end of the QRS complex to the end of the T wave.
  • Photoelectric volume pulse wave is a wave formed by detecting the change of blood vessel volume in living tissue by means of photoelectric means. Referring to Fig. 6, the pulse wave signal is also close to periodicity. Deterministic signal.
  • Pulse transit time (PTT) and pulse arrival time (PAT) are commonly used as parameters to determine blood pressure based on PPG and ECG signals.
  • PTT Pulse Transmit Time
  • PTT pulse arrival time
  • the PTT can be calculated by simultaneously acquiring the delay times of the two electrodes ECG and PPG, thereby indirectly obtaining the blood pressure value.
  • the R wave peak point of the ECG is extracted as the starting point of the PTT, and the feature point of the PPG signal is taken as the end point of the PTT. As shown in FIG.
  • the pulse wave arrival time is the delay between the R wave peak point in the ECG waveform and the corresponding feature point in the PPG waveform, for example, PAT f is the R wave peak point and the PPG waveform in the ECG waveform.
  • PAT p is the delay between the peak of the R-wave in the ECG waveform and the peak in the PPG waveform.
  • the heart rate is measured by a pulse wave (PPG) signal. If the acquisition time is too short, the number of PPG waveforms may be insufficient, resulting in the subsequent heart rate algorithm being unable to calculate or the accuracy is very low. If the acquisition time is too long, it is easy to cause the user. Time is wasted, because the accuracy of the algorithm has been optimized after collecting a certain number of PPG waveforms, and the subsequent PPG waveforms have little improvement on the accuracy of the algorithm, and may even introduce noise to affect the algorithm results.
  • PPG pulse wave
  • the number of feature reference points of the acquired biosignal may be detected, and when the number of feature reference points reaches a certain number, the biosignal is stopped.
  • the feature reference point includes a peak point, a valley point or other reference points.
  • the characteristic reference point of the PPG signal the peak or trough of the pulse wave
  • the pulse wave signal is acquired, and the heart rate is calculated according to the time required to acquire the N complete pulse wave waveforms.
  • PAT pulse arrival time
  • PAT p is a delay between a peak point of an R wave in an ECG waveform and a peak in a PPG waveform, and is detected in the ECG waveform.
  • the acquisition of the PPG and the ECG signal is stopped, and the PAT p is obtained.
  • the average value is used as an input parameter for calculating the blood pressure value.
  • step 501 the method for acquiring motion sensor data in step 501 and the method for identifying the activity type and activity intensity of step 502 and the above-mentioned step S210 (FIG. 2) and step 301 are shown. (Fig. 3) respectively correspond to the same.
  • the acquisition period of the biosignal is segmented into a number of shorter time intervals, and an average signal to noise ratio of the biosignal data collected by each biosensor at each time interval is obtained; and when When the average signal to noise ratio is greater than the set decision threshold, the biosignal data at the time interval is stored as valid biosignal data.
  • cortical EEG signals when measuring cortical EEG signals, cortical EEG signals are relatively low in signal-to-noise and are susceptible to interference from ocular electricity, myoelectricity, and other noise. If the average signal-to-noise ratio of the cortical EEG signal at a certain time interval is less than the set threshold, the time interval is regarded as an invalid acquisition period, and the interval is an effective acquisition period. The cortical EEG signal is collected until the sum of the effective acquisition periods is equal to the set duration, and the acquisition of the cortical EEG signal is stopped.
  • the heart rate when measuring the heart rate, extract the feature reference point (the peak or trough of the pulse wave) on each cycle of the PPG signal, and the average signal-to-noise ratio of the PPG signal data of a certain period is smaller than the set due to the interference of motion noise or the like.
  • the decision threshold is used as the invalid feature reference point of the PPG signal feature reference point on the period, and is not used as an input for calculating the heart rate.
  • the pulse wave signal is stopped, and the heart rate is calculated according to the K valid complete pulse wave waveforms. For example, every two effective feature reference points can be calculated.
  • the inverse of the time interval is used to calculate the instantaneous heart rate.
  • FIG. 10 is a schematic structural diagram of a biological signal collection apparatus according to an embodiment of the present invention.
  • the biosignal acquisition device provided in this embodiment can implement various steps of the biosignal acquisition method applied to the biosignal acquisition device provided by any embodiment of the present invention, and the specific implementation process is not described herein.
  • the biosignal collection device provided in this embodiment specifically includes:
  • An obtaining unit 71 configured to acquire output data of the at least one motion sensor
  • the control unit 72 is configured to control, according to at least the output data of the at least one motion sensor, a collection duration of the at least one biosignal of the user;
  • the collecting unit 73 is configured to collect the at least one biosignal of the user within a collection duration of the at least one biosignal.
  • control unit 72 is specifically configured to:
  • the at least one biosignal has a periodicity
  • the control unit 72 is further configured to:
  • the motion sensor includes any one of an accelerometer, a gyroscope, a pressure sensor, a microphone, a magnetometer, and an altimeter.
  • the activity type includes any one of running, walking, cycling, swimming, climbing, standing, sitting, sleeping.
  • biosignal acquisition device herein is described by using a functional unit, which may be an application specific integrated circuit (Application Specific Integrated). Circuit, ASIC), electronic circuit, processor.
  • ASIC Application Specific Integrated
  • the biosignal acquisition device herein may be the electronic device 100 of FIG. 1, the acquisition unit 71 and the control unit 72 may be implemented by the processor 101 and the memory 108, and the acquisition unit 73 may be implemented by the biosignal sensor 103.
  • FIG. 11 is a schematic structural diagram of still another electronic device involved in the above embodiment. The following describes the components of the electronic device 700 in detail with reference to FIG. 11. Referring to FIG. 11, the electronic device 700 includes:
  • the at least one motion sensor 701 is configured to monitor a user's motion, and the output data is used as an input of a subsequent control biosignal acquisition duration algorithm;
  • the memory 702 is configured to store instructions or data
  • the processor 703 is configured to: acquire output data of the at least one motion sensor 701, and control an acquisition duration of at least one biosignal of the user according to at least the output data of the at least one motion sensor 701;
  • the processor 703 is specifically implemented by performing FIGS. 2 through 4, which relate to the process of acquiring output data of a motion sensor and controlling the duration of biosignal acquisition and/or other processes for the techniques described herein.
  • At least one biosensor 704 configured to acquire the at least one biosignal of the user during an acquisition duration of the at least one biosignal.
  • Figure 11 only shows a simplified design of the electronic device.
  • the electronic device 700 may include an input module, a display module, and a communication module of the electronic device 100 in FIG. 1, and all of the electronic devices that can implement the present invention are within the scope of the present invention.
  • FIG. 12 is a schematic illustration of a system in accordance with one embodiment of the present invention.
  • system 800 includes a device 820 for measuring biosignals and a device 830 for measuring motion signals, which may be connected by wireless or by wire.
  • the device 820 and the device 830 may include constituent elements of the electronic device shown in FIG. 1.
  • System 800 includes at least one motion sensor 812, memory 805, processor 801, And at least one biosensor 802; wherein the at least one motion sensor 812 is configured to monitor a user's motion;
  • the memory 805 is configured to store instructions or data
  • the processor 801 is coupled to the memory 805, the processor 801 is configured to: acquire output data of the at least one motion sensor; and control at least one creature of the user according to at least the output data of the at least one motion sensor The duration of the signal acquisition;
  • the at least one biosensor 802 is configured to collect the at least one biosignal of the user during an acquisition duration of the at least one biosignal.
  • the at least one motion sensor 812 is coupled to the processor 801 via a wireless interface.
  • the at least one motion sensor 812 and the processor 801 are coupled by a wired interface.
  • the at least one biosensor 802 and the processor 801 are coupled through a wireless interface.
  • the at least one biosensor 802 and the processor 801 are coupled by a wired interface.
  • the at least one motion sensor 812 and the processor 801 are disposed in the same device, or are respectively disposed in different devices.
  • the at least one biosensor 802 and the processor 801 are disposed in the same device, or are respectively disposed in different devices.
  • device 830 can be coupled to a user's leg, and device 820 can be attached to the user's arm.
  • Motion sensor 812 is for receiving activity data
  • biosensor 802 is for detecting biosignal
  • motion sensor 812 is correspondingly coupled to a processor 810 that receives activity data from the motion sensor.
  • Biosensor 802 is correspondingly coupled to a processor 801 that receives biosignals from a biosensor.
  • Processor 810 then provides data to its corresponding communication module 818.
  • Apparatus 820 includes a communication module 803 that receives data from communication module 818 and a memory 805 that includes an acquisition duration control algorithm.
  • Processor 801 also performs Figures 2 through 4, which relate to obtaining the output of the motion sensor Data and processing to control the duration of biosignal acquisition and/or other processes for the techniques described herein.
  • Figure 12 only shows a simplified design of the device 820 for measuring biosignals and the device 830 for measuring motion signals.
  • the device 820 and the device 830 may also include any number of processors, memories, communication modules, sensors, etc., and the device 820 and the device 830 may further include the input module and the display module in FIG. 1 , all of which may implement the present invention.
  • Equipment is within the scope of the invention.
  • FIG. 13 is a schematic diagram of a specific application scenario of a system according to another embodiment of the present invention.
  • Device 905 can be a mobile electronic device such as a cell phone, tablet, or other similar electronic device as mentioned above.
  • the device 905 can collect data of the motion sensor from the wearable device 901 and the wearable device 902, and the device 905 utilizes the biosignal acquisition duration control based on the collected data of the motion sensor.
  • the algorithm calculates the optimal measurement duration for each biosignal and provides it to the wearable device 903 and the wearable device 904 that measure the biosignal, for example, the wearable device 903 is used to measure the pulse wave, the wearable device 904 is used to measure the electrocardiogram.
  • the wearable device 903 can collect the data of the motion sensor directly from the wearable device 901 and the wearable device 902.
  • the biosignal duration control algorithm is used to control the biosignal measurement duration.
  • the communication between any two of the device 905, the wearable device 901, the wearable device 902, the wearable device 903, and the wearable device 904 may employ a wired or wireless communication protocol.
  • a wired or wireless communication protocol such as Bluetooth, ZigBee, ANT; or a long-distance wired communication protocol, such as a protocol in the field of computer communication such as TCP/IP.
  • the wearable device in FIG. 13 can include a wristband, a watch, a ring, a button, and the like, and can be worn on any part of the human body, which is not limited in the present invention.
  • the processor for performing the above electronic device, system of the present invention may be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), and a field programmable gate array (FPGA). Or other programmable logic devices, transistor logic Device, hardware component or any combination thereof. It is possible to implement or carry out the various illustrative logical blocks, modules and circuits described in connection with the present disclosure.
  • the processor may also be a combination of computing functions, for example, including one or more microprocessor combinations, a combination of a DSP and a microprocessor, and the like.
  • the steps of a method or algorithm described in connection with the present disclosure may be implemented in a hardware, or may be implemented by a processor executing software instructions.
  • the software instructions may be comprised of corresponding software modules that may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable hard disk, CD-ROM, or any other form of storage well known in the art.
  • An exemplary storage medium is coupled to the processor to enable the processor to read information from, and write information to, the storage medium.
  • the storage medium can also be an integral part of the processor.
  • the processor and the storage medium can be located in an ASIC. Additionally, the ASIC can be located in the user equipment.
  • the processor and the storage medium may also reside as discrete components in the user equipment.
  • the functions described herein can be implemented in hardware, software, firmware, or any combination thereof.
  • the present invention can be implemented in a combination of hardware or hardware and computer software in combination with the elements and algorithm steps of the various examples described in the embodiments disclosed herein. Whether a function is implemented in hardware or computer software to drive hardware depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods for implementing the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present invention.
  • the functions may be stored in a computer readable medium or transmitted as one or more instructions or code on a computer readable medium.
  • Computer readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one location to another.
  • a storage medium may be any available media that can be accessed by a general purpose or special purpose computer.

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Abstract

一种生物信号采集方法、装置、电子设备（100）及系统（800）。该生物信号采集方法包括：获取至少一个运动传感器的输出数据（S210）；至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长（S220）；在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号（S230）。该生物信号采集方法、装置、电子设备（100）及系统（800）能够合理控制生物信号采集时长，提高生物信号的测量精度。

Description

一种生物信号采集方法、装置、电子设备及系统 技术领域
本发明实施例涉及通信技术领域，并且更具体地，涉及一种生物信号采集方法、装置、电子设备及系统。
背景技术
伴随着人口老龄化、亚健康、环境污染等问题的出现，人们对健康的要求和关注程度越来越高。互联网、智能终端、可穿戴设备以及医疗信息化的快速发展，使得移动健康成为一个重要的发展方向，国内外市场越来越重视移动健康设备的开发和推广。移动健康中，用户的生物信号采集是很重要的一个方面，它是后续整个信息处理过程的起点。
生物信号采集时长是一个关键指标，采集时长太长，会影响用户体验；采集时长太短，有可能获取到的信号值的数量不足，导致误差较大，精度难以保证。目前，移动健康市场的可穿戴设备在采集生物信号时，均是使用固定时长的方法，多是由产品的市场要求或者技术专家的意见来确定，主观性较强。因此，有必要合理确定生物信号的采集时长，以兼顾用户体验和设备精度的要求。
发明内容
本申请提供了一种生物信号采集方法、装置、电子设备及系统，以实现合理控制生物信号采集时长，提高生物信号的测量精度。
第一方面，本申请的实施例提供一种生物信号采集方法，该方法包括：获取至少一个运动传感器的输出数据；至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。用户处于运动状态时容易受到肌电噪声和运动伪迹的影响，此时生物信号质量较非运动状态时的质量差，基于运动传感器采集的 用户的运动状态信息控制用户的生物信号采集时长可以灵活控制生物信号采集时长，进而提高生物信号的测量精度。运动传感器可以包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种。
根据第一方面，在所述生物信号采集方法的第一种可能的实现方式中，可以至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长，按照所述第一时长采集所述用户的至少一种生物信号。其中，活动类型可以包括各种示例，如跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉等。通常，任何描绘了用户的动作和/或移动的情况都可称为“活动”。对于同一生物信号，它在不同的活动类型下和不同的活动强度的变化情况是有区别的，受肌电噪声和运动伪迹的影响也会有所区别，根据用户当前的活动类型和/或活动强度选择合适的采集时长可以进一步提高生物信号测量的准确性。
根据第一方面，在所述生物信号采集方法的第二种可能的实现方式中，所述至少一种生物信号具有周期性，例如心电图(ECG)、脉搏波(PPG)等。对于周期性生物信号，通常需要采集足够个数的完整波形才能保证测量准确性，为了得到足够数量的完整波形，可以检测采集的生物信号的特征参考点的个数，当特征参考点个数达到一定数量时停止采集生物信号，保证测量的精确性。与上述第一方面的第一种可能的实现方式类似，可以至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一值，检测所述至少一种生物信号的特征参考点个数，当所述特征参考点个数等于所述第一值时，停止采集所述至少一种生物信号。根据用户当前的活动类型和/或活动强度选择合适的采集时长可以进一步提高周期性生物信号测量的准确性。
第二方面，本申请的实施例提供一种生物信号采集装置，该采集 装置具有实现上述第一方面或者以上第一方面的任意一种实现方式中的方法的功能。所述功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。所述硬件或软件包括一个或多个与上述功能相对应的模块。在一个可能的设计中，该装置包括：获取单元，用于获取至少一个运动传感器的输出数据；控制单元，用于至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；采集单元，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
第三方面，本申请的实施例提供一种电子设备，该电子设备具有实现上述第一方面或者以上第一方面的任意一种实现方式中的方法的功能。所述功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。所述硬件或软件包括一个或多个与上述功能相对应的模块。
在一个可能的设计中，该电子设备包括：至少一个运动传感器，用于监测用户的运动；存储器，用于存储指令或数据；处理器，与所述存储器耦合，所述处理器用于实现上述第一方面或者以上第一方面的任意一种实现方式中的以下功能：获取至少一个运动传感器的输出数据以及至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；和，至少一个生物传感器，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
第四方面，本申请的实施例提供一种生物信号采集系统，该系统具有实现上述第一方面或者以上第一方面的任意一种实现方式中的方法的功能。该系统包括：至少一个运动传感器，用于监测用户的运动；存储器，用于存储指令或数据；处理器，与所述存储器耦合，所述处理器用于实现上述第一方面或者以上第一方面的任意一种实现方式中的以下功能：获取所述至少一个运动传感器的输出数据；并至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；至少一个生物传感器，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
根据第四方面，在所述生物信号采集系统的第一种可能的实现方式中，所述至少一个运动传感器与所述处理器通过无线接口耦合。
根据第四方面，在所述生物信号采集系统的第二种可能的实现方式中，所述至少一个运动传感器与所述处理器通过有线接口耦合。
根据第四方面，或以上第四方面的第一种或第二种实现方式，在所述生物信号采集系统的第三种可能的实现方式中，所述至少一个生物传感器与所述处理器通过无线接口耦合。
根据第四方面，或以上第四方面的第一种或第二种实现方式，在所述生物信号采集系统的第四种可能的实现方式中，所述至少一个生物传感器与所述处理器通过有线接口耦合。
根据第四方面，或以上第四方面的任一种实现方式，在所述生物信号采集系统的第五种可能的实现方式中，所述至少一个运动传感器与所述处理器设置于同一设备中，或分别设置于不同的设备中。
根据第四方面，或以上第四方面的任一种实现方式，在所述生物信号采集系统的第六种可能的实现方式中，所述至少一个生物传感器与所述处理器设置于同一设备中，或分别设置于不同的设备中。
第五方面，本发明实施例提供了一种计算机存储介质，用于储存为上述电子设备所用的计算机软件指令，其包含用于执行上述第一方面或者以上第一方面的任意一种实现方式中的方法所设计的程序。
相较于现有技术，本发明提供的方案可以灵活控制生物信号采集时长，提高生物信号测量精度。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的一种电子设备的结构示意图；
图2是本发明实施例提供的一种生物信号采集方法的流程图；
图3是本发明实施例提供的另一种生物信号采集方法的流程图；
图4是本发明实施例提供的一种活动类型识别方法的流程图；
图5是心电图信号示意图；
图6是脉搏波信号示意图；
图7是一个完整心电信号波形示意图；
图8是脉搏到达时间示意图；
图9是本发明实施例提供的又一种生物信号采集方法的流程图；
图10是本发明实施例提供的一种生物信号采集装置的结构示意图；
图11是本发明实施例提供的又一种电子设备的结构示意图；
图12是本发明实施例提供的一种生物信号采集系统的结构示意图；
图13是根据本发明另一个实施例的生物信号采集系统的具体应用场景示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
图1为根据本发明一种实施例的电子装置的模块图。参照图1，电子设备100可包括处理器101、总线104、存储器108和传感器102。
存储器108可包括一个或多个存储介质，例如包括硬盘驱动器、固态驱动器、闪速存储器、诸如只读存储器("ROM")之类的永久存储器、诸如随机存取存储器("RAM")之类的半永久存储器、任何其它合适类型的存储组件、或者它们的任意组合。存储器108可以是内置存储器或外置存储器。内置存储器可包括如动态随机存储器 (DRAM：dynamic RAM)、静态随机存储器(SRAM：static RAM)、同步动态随机存储器(SDRAM synchronous dynamic RAM)之类的易失性存储器或者如一次性可编程只读存储器(OTPROM：one time programmable ROM)、可编程只读存储器(PROM：programmable ROM)、可擦除可编程只读存储器(EPROM：erasable and programmable ROM)、电可擦可编程只读存储器(EEPROM：electrically erasable and programmable ROM)、掩膜只读存储器(mask ROM)、快闪只读存储器(flash ROM)、快闪记忆体(NAND flashmemory)、编码型快闪记忆体(NOR flashmemory)之类的非易失性存储器(non-volatile Memory)中的至少一种。此时，内置存储器也可以取固态驱动器(SSD：Solid State Drive)的形态。外置存储器可包括小型闪存(CF：compact flash)、安全数码(SD：secure digital)、微型安全数码(Micro-SD：micro secure digital)、迷你安全数码(Mini-SD：mini secure digital)、极端数码(xD：extreme digital)或记忆棒(memory stick)中的至少一种。
可选地，电子设备100包括不止一个传感器(例如，图1中的传感器102和传感器103)。例如，传感器102为运动传感器，用于监测用户的运动，传感器102可包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种，还可以包括亮度传感器、光学传感器、接近传感器中的任意一种，任何用于监测用户的运动的传感器都可以称为运动传感器，因而所引用的示例不应解释为是对本公开构成限制。例如，传感器103为生物传感器，用于监测用户的生物信号，传感器103可以包括嗅觉传感器(E-nose sensor)、EMG传感器(electromyography sensor，肌电图传感器)、EEG传感器(electroencephalogram sensor，脑电图传感器)、ECG传感器(electrocardiogramsensor，心电图传感器)或指纹传感器。并且，传感器102和传感器103可量测物理量或感测电子装置的工作状态，并将量测或感测的信息转换为电信号。
可选地，电子设备100还包括输入模块105，输入模块105可从用户接收命令或数据，并通过总线104传递给处理器101或存储器 108。例如，输入模块105可包括触摸板(touchpanel)、键(key)、或超声波输入装置。触摸板可通过电容式、感压式、红外线方式、或超声波方式中的至少一种方式识别触摸输入。其中，触摸板还可以包括控制器。对于电容式而言，不仅可以识别直接触摸，而且还可以识别靠近。所述触摸板还可以包括触觉层(tactile layer)。此时，触摸板可以给用户提供触觉反应。键可以包括键盘或触摸键，超声波输入装置为可通过产生超声波信号的笔而在电子装置中感测出微声波而确认数据的装置，可用来实现无线识别。
可选地，电子设备100还包括显示模块106，显示模块106可将图形、图像或数据显示给用户。例如，显示模块106可包括面板。例如，面板可以是LCD(liquid-crystal display，液晶显示器)、LED(light emitting diode display，发光二极体面板)、AMOLED(active-matrix organic light-emitting diode，主动矩阵有机发光二极体面板)。并且，面板可以被构成为柔软(flexible)、透明(transparent)或者可穿戴(wearable)。其中，面板也可以与触摸板构成为一个模块。另外，显示模块106还可以包括用于控制面板的控制电路。
可选地，电子设备100还包括通信模块107，以使设备100可以利用任何适当的通信协议与一个或多个其它电子装置或服务器(未示出)通信。其中，通信模块107可支持如Wi-Fi(wireless fidelity，无线保真)、蓝牙(BT：Bluetooth)、近场通信(NFC：near field communication)之类的近距离通信协议或者如因特网(Internet)、局域网(LAN：local area network)、广域网(WAN：wire area network)、远程通信网络(telecommunication network)、蜂窝网络(cellular network)、卫星网络(satellite network)。通信模块107还可包括使电子设备100能够与另一个设备(例如，计算机)耦合，通过有线或无线方式与所述另一个设备通信的电路。
总线104可以是将电子设备100所包括的构成要素(例如，处理器101、存储器108、传感器102、传感器103、输入模块105、显示模块106)相互连接起来，并使构成要素之间实现通信的电路。
处理器101用于执行指令(例如，从输入模块105获取的指令)、中断处理、定时和其他功能。另外，处理器101可进一步包括图形处理单元(graphic processing unit)。
存储器108可存储处理器101或其他构成要素(例如，从输入模块105、显示模块106、通信模块107)接收或由所述处理器101或其他构成要素产生的指令或数据。此时，存储器108可包括内部缓冲器和外部缓冲器。
并且，存储器108还可以包括内核、中间件、应用程序接口(API application programming interface)。内核可对用于执行其他程序模块(例如中间件、API或应用)实现的动作或功能的系统资源(例如总线104、处理器101、或存储器108)进行控制或管理。而且，内核可以提供用于从中间件、API或应用访问电子设备100的个别构成要素而进行控制或管理的接口。中间件可以执行中介作用，以使API或应用能够与内核进行通信而交换数据。并且，中间件可对从一个以上的应用接收的作业请求分配使用电子设备100的系统资源(例如总线104、处理器101或存储器108)的优先顺序，从而可以执行针对作业请求的负载均衡(load balancing)。API为用于通过应用控制由内核或中间件提供的功能的接口，可包括用于文件控制、窗口控制、图像处理或文字控制的至少一个接口或函数。
图2为本发明一个实施例提供的一种生物信号采集方法的流程图，本实施例提供的方法可以应用在图1所示的电子设备100中，电子设备100可以包括，但不限于，可穿戴设备和其它便携式和非便携式计算设备，例如，智能手环、智能手表、智能电话、平板电脑和膝上型电脑等。请参照图2，包括如下步骤：
步骤S210：获取至少一个运动传感器的输出数据。
在本实施例的一个可选实施方式中，通过至少一个运动传感器(例如，图1中的传感器102)获取用户的运动状态信息。在一些实施例中，运动传感器包括加速度计、陀螺仪和磁力计，其中加速度计、陀螺仪和磁力计均可以测量三维空间三个轴向的数据变化，组成了一 个9轴姿态检测传感器。在一些实施方式中，可以将运动传感器102实现为微机电系统(MEMS)。
在一些实施例中，运动传感器的输出数据为原始数据，在另一些实施例中，运动传感器的输出数据为经过处理后的数据，例如，通过多个运动传感器的输出数据计算出的电子设备的运动方向及运动速度。
在本实施例的一个可选实施方式中，按照预设的时间周期获取至少一个运动传感器的输出数据，例如每隔1～3秒获取一次用户的运动状态信息。
在本实施例的另一个可选实施方式中，只采集预设时间段内(例如，5秒钟)的至少一个运动传感器的输出数据供后续信息处理使用。
步骤S220：至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长。
在一些实施例中，电子设备包括能检测用户的生物信号的至少一种生物信号传感器(例如，图1中的传感器103)，所述生物信号包括心电图(ECG)、脑电图(EEG)、肌电图(EMG)、生物电阻抗、体温、血糖、血氧、血压、光电容积脉搏波(PPG)等。
用户主动启动具有特定功能的生物信号传感器以开始收集生物信号信息，或者电子设备控制生物传感器周期性自动检测某些生物信号信息。该信息能存储在设备自身上、或通过与其它设备共享或者通过网络通信传输至远程设备。例如，收集ECG和心率数据的用户可需要利用双手触碰若干干式传感器(dry sensor)或者能够使用允许通过仅将传感器置于接近胸部来收集ECG和心率数据的电容(例如，非接触式)传感器，或者能够使用氧指标传感器(oxymetric sensor)来在指尖测量心率。
步骤S230：在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
如图3所示，在一种可能的实现方式中，步骤S220具体可以包括以下步骤：
步骤301、至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项。
步骤302、获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长。
步骤303、按照所述第一时长采集所述用户的至少一种生物信号，当所述第一时长到时，停止采集所述至少一种生物信号。
在一个实施例中，术语“活动”可包括各种示例，如跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉等。通常，任何描绘了用户的动作和/或移动的情况都可称为“活动”，因而所引用的示例不应解释为是对本公开构成限制。
对于同一生物信号，它在不同的活动类型和活动强度下的变化情况是有区别的，受肌电噪声和运动伪迹的影响也会有所区别，根据用户当前进行的活动类型或者根据用户当前的活动强度选择合适的采集时长可以提高生物信号测量的准确性。
在一种可能的实现方式中，步骤301可以按照图4所示的方法判断用户活动类型，参见图4，包括以下步骤：
步骤401：对至少一个运动传感器的输出数据进行滤波处理。
运动传感器的输出数据通常包含许多噪声，获取到的至少一个运动传感器(例如，图1中的传感器102)的输出数据可以经过滤波处理或者其他处理删掉一部分数据。例如，通过滤波处理消除或者减少与震动有关的数据，其中，震动是由汽车、火车或者轮船造成的，例如人在一辆发动机已经启动但并未行驶的汽车上时，人实际上是处于静止状态的，但是惯性传感器仍然会产生传感数据。又如，人在交谈时，会有轻微的肢体动作，这时惯性传感器也会产生于震动有关的数据。
步骤402：根据所述至少一个运动传感器的输出数据计算特征值。
例如，采集诸如陀螺仪或加速度计的惯性传感器的数据，从而根据每一组数据计算出一组特征值。在一种可能的实现方式中，特征值包括从信号源计算出的信号均值或标准差。下面以加速度计为例简要 说明特征值的计算方法，加速度计检测出X、Y、Z轴三方向的加速度值(X，Y，Z)，可以根据以下公式计算在采集周期T的特征值：
Figure PCTCN2015096362-appb-000001
Figure PCTCN2015096362-appb-000002
其中，n为采样周期T内的采样点个数，i为采样点序号，X(i)为采样点信号值，1≤i≤n，
Figure PCTCN2015096362-appb-000003
为均值。
步骤403：根据步骤402计算出的特征值判断用户的活动类型。
可选地，可以将步骤402计算出的特征值与阈值比较，判断用户的活动类型。例如，如果加速度计标准差高于某一阈值A，那么用户被假定为正在跑步；否则，如果加速度计标准差高于阈值B低于阈值A，那么用户被假定为正在步行，否则，用户被假定为正在站立或静坐，据此可以将用户的活动类型彼此区分开来。
为提高活动类型识别的准确性，可以应用现代机器学习的方法，以人体运动传感器数据和正确的活动类型作为输入，通过机器学习模型进行训练，获得人体运动识别模型，并通过这些人体运动特征识别人体的活动类型，获得与该传感器数据相对应的识别的活动类型，提高对活动类型的识别率。
在一种可能的实现方式中，还可以根据其他有用信息识别用户的活动类型或活动强度，其中，其他有用信息包括导航信息(例如，位置、速度信息)、输入设备信息(例如，麦克风捕获的音频数据、摄像头捕获的图像信息)、确定的上下文(例如，通过用户的触摸屏幕操作、按键操作、发出声音等方式确定上下文，以确定用户的活动类型)。例如，根据GPS模块的输出数据判断用户的速度、位置和所经路径，据此对用户的活动类型进行更加精确的判断。
在一种可能的实现方式中，对于以预定周期进行的具有节律性的反复运动，例如，在跑步中，可确定单位时间的步数(跑步频率)；而在游泳中，可确定单位时间的划动数。根据运动频率、反复运动中每次的移动量以及被检者的体重计算出活动强度。假设运动为跑步，则 跑步频率与步幅的乘积求得的跑步速度可作为活动强度。又如，假设运动为游泳，则挥臂频率与挥臂幅度的乘积可作为活动强度。活动强度还可以通过呼吸频率或者单位时间内的耗氧量等参数来表征，所引用的示例不应解释为是对本公开构成限制。可以建立用户活动强度(或活动类型)与生物信号采集时长的映射关系表，当需要获知生物信号采集时长时通过查表的方式获取与当前活动强度(或活动类型)最匹配的采集时长。对于同一种活动类型，它可能对应多种活动强度，在不同的活动强度下的采集时长也会有所区别，以跑步为例，构建如表一所示的映射关系表，查找并获取与用户当前活动状态相匹配的采集时长。
表一
Figure PCTCN2015096362-appb-000004
在另一种可能的实现方式中，可以将用户的活动强度按等级划分，例如，设置两个加速度阈值A1、A2，A1<A2，如果加速度值小于阈值A1，那么用户活动强度等级为低，记为L；否则，如果加速度值高于阈值A1，小于阈值A2，那么用户活动强度等级为中，记为M；如果加速度值高于阈值A2，用户运动等级为高，记为H。随着用户活动强度等级的增大，噪声也会随之增大，因此需要的生物信号采集时长也逐渐增大。
一种活动类型可能对应多种活动强度等级，例如，假设运动为跑步，则用户可以慢跑、中跑或快跑。又如，假设运动为走路，则用户可以是慢步、中步或快步。在某些实施例中，可以同时判断用户的活动类型和活动强度等级，据此获得与用户当前活动状态最匹配度的生 物信号采集时长。以跑步为例，构建如表二所示的映射关系表，查找并获取与用户当前活动状态相匹配的采集时长。
在一个可能的实现方式中，可以将用户的活动类型分为静止和运动两种，用户处于运动状态时容易受到肌电噪声和运动伪迹的影响，此时生物信号质量较非运动状态时的质量差，相较于静止状态可以设置较长的采集时间以提高生物信号测量的精度。
表二
Figure PCTCN2015096362-appb-000005
在某些实施例中，生物信号可以是具有周期性的信号，并可包括例如心电图(ECG)、脉搏波(PPG)或其他具有周期的信号。例如，生物信号可对应于如图5所示的ECG波形，或者如图6所示的PPG波形。
ECG波形可以是具有周期性PQRST波形的重复图案的准周期性信号，由P波，QRS波和T波等组成(图7)，T波之后还包括U波(低电压小波，未示出)，其中P波表示心房除极过程，QRS波群表示心室的除极过程，T波表示心室的复极过程。QRS波群包括三个紧密相连的波，第一个向下偏折的波称为Q波，继Q波后的一个高尖的直立波称为R波，R波后向下偏折的波称为S波，正常QRS波群时间为0.06～0.10秒。PR间期指由P波起点到QRS波群起点间的时间。一般成人PR间期为0.12～0.20秒，PR间期随心率与年龄而变化，一般年龄越大其PR间期越长。S-T段是指自QRS波群的终点至T波起点的一段水平线。QT区间是指QRS波群到T波结束的一段时间。
光电容积脉搏波(PPG)是借助光电手段在活体组织中检测血管容积变化形成的一种波，参见图6，脉搏波信号也是一种接近于周期性 的确定性信号。
脉搏传输时间(PTT)和脉搏到达时间(PAT)通常用作参数以基于PPG和ECG信号确定血压。例如，基于脉搏波传输时间(Pulse Transmit Time)，即PTT，与血压的线性模型，可以通过同步采集到的两电极ECG和PPG的延迟时间计算PTT，进而间接地得到血压值。提取ECG的R波峰值点作为PTT的开始点，PPG信号的特征点作为PTT的结束点。如图8所示，脉搏波到达时间(PAT)为ECG波形中的R波峰值点与PPG波形中相应特征点之间的延迟，例如，PATf为ECG波形中的R波峰值点与PPG波形中波谷之间的延迟，PATp为ECG波形中的R波峰值点与PPG波形中波峰之间的延迟。
对于周期性生物信号，通常需要采集足够个数的完整波形才能保证测量准确性。例如，通过脉搏波(PPG)信号来测量心率，若采集时长太短，则PPG波形的个数可能不足，造成后续心率算法无法计算或者精度很低，若采集时长太长，则容易造成用户的时间浪费，因为在采集一定个数的PPG波形后，算法精度已经达到最佳，后续的PPG波形对算法精度提高不大，甚至可能导入噪声影响算法结果。另一方面，由于用户心率不同，心率高的用户仅需要提供较短时间的生物信号就能够包含足够个数的PPG波形，满足算法的输入要求，而心率低的用户要提供同样个数的PPG波形信号，需要的采集时长会较长。
在一些实施例中，对于具有周期性的生物信号，为了得到足够数量的完整波形，可以检测采集的生物信号的特征参考点的个数，当特征参考点个数达到一定数量时停止采集生物信号，保证测量的精确性。其中，特征参考点包括波峰点、波谷点或其他参考点。
例如，测量心率时，提取PPG信号的特征参考点(脉搏波的波峰或者波谷)，确定采集到的完整脉搏波个数，当特征参考点个数为N(例如，N＝15)时，停止采集脉搏波信号，根据采集N个完整脉搏波波形所需的时间计算心率。
又如，基于PPG和ECG信号测量血压时，假设以脉搏到达时间 (PAT)为输入参数，PATp为ECG波形中的R波峰值点与PPG波形中波峰之间的延迟，检测ECG波形中的R波峰值点和PPG波形的波峰点的个数，当R波峰值点和PPG波形的波峰点的个数为M(例如，M＝10)时停止采集PPG和ECG信号，求得PATp的平均值作为计算血压值的输入参数。
下面结合图9对周期性生物信号的采集流程进行说明，参见图9，步骤501获取运动传感器数据的方法和步骤502活动类型和活动强度的识别方法与上述的步骤S210(图2)和步骤301(图3)分别对应相同。
获取到与所述用户的活动类型和活动强度中的至少一项相匹配的第一值(503)后，采集生物信号并计算所述至少一种生物信号的特征参考点个数(504)，当所述特征参考点个数等于所述第一值时(505)，停止采集所述至少一种生物信号(506)。
在某些实施例中，将生物信号的采集时段分割为若干个较短的时间间隔，获取每个时间间隔上的每一个生物传感器采集到的生物信号数据的平均信噪比；并当所述平均信噪比大于设定的判决阈值时，将所述时间间隔上的生物信号数据存储为有效生物信号数据。
例如，测量皮层脑电信号时，因为皮层脑电信号信噪比较低，容易受到眼电、肌电以及其他噪声的干扰。如果某个时间间隔上的皮层脑电信号的平均信噪比小于设定阈值，则将该段时间间隔作为无效采集时段，反之，该段时间间隔为有效采集时段。采集皮层脑电信号，直至有效采集时段之和等于设定时长，停止采集皮层脑电信号。
又如，测量心率时，提取PPG信号每个周期上的特征参考点(脉搏波的波峰或者波谷)，由于运动噪声等的干扰使得某个周期的PPG信号数据的平均信噪比小于设定的判决阈值，则将所述周期上的PPG信号特征参考点作为无效特征参考点，不作为后续计算心率的输入。当有效的特征参考点个数为K(例如，K＝10)时，停止采集脉搏波信号，根据采集K个有效的完整脉搏波波形计算心率，例如，可以计算每两个有效特征参考点之间的时间间隔的倒数来计算瞬时心率。
图10为本发明实施例提供的一种生物信号采集装置的结构示意图。如图10所示，本实施例提供的生物信号采集装置可以实现本发明任意实施例提供的应用于生物信号采集装置的生物信号采集方法的各个步骤，具体实现过程在此不再赘述。本实施例提供的生物信号采集装置具体包括：
获取单元71，用于获取至少一个运动传感器的输出数据；
控制单元72，用于至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；
采集单元73，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
在本实施例的一个可选实施方式中，所述控制单元72具体用于：
至少根据获取单元71获取的至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长，按照所述第一时长采集所述用户的至少一种生物信号，当所述第一时长到时，停止采集所述至少一种生物信号。
在一些实施例中，所述至少一种生物信号具有周期性，对于周期性生物信号，所述控制单元72还用于：
至少根据获取单元71获取的至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一值，检测所述至少一种生物信号的特征参考点个数，当所述特征参考点个数等于所述第一值时，停止采集所述至少一种生物信号。
可选地，本实施例中，运动传感器包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种。
可选地，本实施例中，所述活动类型包括跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉中的任一种。
可以理解的是，这里的生物信号采集装置采用功能单元的方式进行描述，功能单元可以是专用集成电路(Application Specific Integrated  Circuit，ASIC)，电子电路，处理器。特别的，这里的生物信号采集装置可以是图1中的电子设备100，获取单元71和控制单元72可以通过处理器101和存储器108实现，采集单元73可以通过生物信号传感器103实现。
图11示出了上述实施例中所涉及的又一种电子设备的结构示意图，下面结合图11对电子设备700的各个构成部件进行具体的介绍，请参照图11，电子设备700包括：
至少一个运动传感器701，存储器702，处理器703以及至少一个生物传感器704。
所述至少一个运动传感器701用于监测用户的运动，其输出数据作为后续控制生物信号采集时长算法的输入；
所述存储器702，用于存储指令或数据；
所述处理器703，用于：获取所述至少一个运动传感器701的输出数据以及至少根据所述至少一个运动传感器701的输出数据控制用户的至少一种生物信号的采集时长；
所述处理器703具体通过执行图2至图4，图9涉及获取运动传感器的输出数据以及控制生物信号采集时长的处理过程和/或用于本申请所描述的技术的其他过程。
至少一个生物传感器704，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
可以理解的是，图11仅仅示出了电子设备的简化设计。在实际应用中，电子设备700可以包含图1中的电子设备100的输入模块、显示模块、通信模块，而所有可以实现本发明的电子设备都在本发明的保护范围之内。
图12是根据本发明一个实施例的一个系统的示意图，参见图12，系统800包括一个测量生物信号的设备820和一个测量运动信号的设备830，两个设备可以通过无线或者有线方式连接。设备820和设备830可以包括图1中所示的电子设备的构成元素。
系统800包括至少一个运动传感器812，存储器805，处理器801， 和至少一个生物传感器802；其中，所述至少一个运动传感器812，用于监测用户的运动；
所述存储器805，用于存储指令或数据；
所述处理器801与所述存储器805耦合，所述处理器801用于：获取所述至少一个运动传感器的输出数据；并至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；
所述至少一个生物传感器802，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
在一种可能的实现方式中，所述至少一个运动传感器812与所述处理器801通过无线接口耦合。
在另一种可能的实现方式中，所述至少一个运动传感器812与所述处理器801通过有线接口耦合。
在一种可能的实现方式中，所述至少一个生物传感器802与所述处理器801通过无线接口耦合。
在另一种可能的实现方式中，所述至少一个生物传感器802与所述处理器801通过有线接口耦合。
可选地，所述至少一个运动传感器812与所述处理器801设置于同一设备中，或分别设置于不同的设备中。
可选地，所述至少一个生物传感器802与所述处理器801设置于同一设备中，或分别设置于不同的设备中。
在一个实例中，设备830可以联接至一位用户的腿上，设备820可以附接至该用户的手臂上。运动传感器812用于接收活动数据，生物传感器802用于检测生物信号，运动传感器812对应地联接至从运动传感器接收活动数据的一个处理器810。生物传感器802对应地联接至从生物传感器接收生物信号的一个处理器801。处理器810然后向其对应的通信模块818提供数据。设备820包括接收来自通信模块818的数据的一个通信模块803和包括采集时长控制算法的一个存储器805。
处理器801还执行图2至图4，图9涉及获取运动传感器的输出 数据以及控制生物信号采集时长的处理过程和/或用于本申请所描述的技术的其他过程。
可以理解的是，图12仅仅示出了测量生物信号的设备820和测量运动信号的设备830的简化设计。在实际应用中，设备820和设备830还可以包含任意数量处理器，存储器，通信模块、传感器等，设备820和设备830还可以包括图1中的输入模块、显示模块，所有可以实现本发明的设备都在本发明的保护范围之内。
图13描绘了根据本发明另一个实施例的一个系统的具体应用场景示意图。设备905可以是移动式电子设备例如手机、平板电脑或者上文中提到的其他类似的电子设备。
在一个可选的实施方式中，设备905可以从可穿戴式设备901和可穿戴式设备902收集运动传感器的数据，设备905至少基于收集到的运动传感器的数据利用上文中的生物信号采集时长控制算法计算出每种生物信号的最佳的测量时长，并提供给测量生物信号的可穿戴式设备903和可穿戴式设备904，例如，可穿戴式设备903用于测量脉搏波，可穿戴式设备904用于测量心电图。
在另一个可选的实施方式中，可穿戴式设备903可以直接从可穿戴式设备901和可穿戴式设备902收集运动传感器的数据利用上文中的生物信号采集时长控制算法控制生物信号测量时长。
设备905、可穿戴式设备901、可穿戴式设备902、可穿戴式设备903和可穿戴式设备904中任意两个设备间的通信可以采用有线或者无线通信协议。例如，可以是短距离的无线通信协议，如蓝牙(Bluetooth)、ZigBee、ANT；或者是长距离的有线通信协议，如采用TCP/IP等计算机通信领域的协议。
可以理解的是，图13中的可穿戴式设备可以包括手环、手表、戒指、纽扣等，可以佩戴在人体的任何部位，本发明对此不做限定。
用于执行本发明的上述电子设备、系统的处理器可以是中央处理器(CPU)，通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)，现场可编程门阵列(FPGA)或者其他可编程逻辑器件、晶体管逻辑 器件，硬件部件或者其任意组合。其可以实现或执行结合本发明公开内容所描述的各种示例性的逻辑方框，模块和电路。所述处理器也可以是实现计算功能的组合，例如包含一个或多个微处理器组合，DSP和微处理器的组合等等。
结合本发明公开内容所描述的方法或者算法的步骤可以硬件的方式来实现，也可以是由处理器执行软件指令的方式来实现。软件指令可以由相应的软件模块组成，软件模块可以被存放于RAM存储器、闪存、ROM存储器、EPROM存储器、EEPROM存储器、寄存器、硬盘、移动硬盘、CD-ROM或者本领域熟知的任何其它形式的存储介质中。一种示例性的存储介质耦合至处理器，从而使处理器能够从该存储介质读取信息，且可向该存储介质写入信息。当然，存储介质也可以是处理器的组成部分。处理器和存储介质可以位于ASIC中。另外，该ASIC可以位于用户设备中。当然，处理器和存储介质也可以作为分立组件存在于用户设备中。
本领域技术人员应该可以意识到，在上述一个或多个示例中，本发明所描述的功能可以用硬件、软件、固件或它们的任意组合来实现。本领域技术人员应该很容易意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，本发明能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。当使用软件实现时，可以将这些功能存储在计算机可读介质中或者作为计算机可读介质上的一个或多个指令或代码进行传输。计算机可读介质包括计算机存储介质和通信介质，其中通信介质包括便于从一个地方向另一个地方传送计算机程序的任何介质。存储介质可以是通用或专用计算机能够存取的任何可用介质。
以上所述的具体实施方式，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具 体实施方式而已，并不用于限定本发明的保护范围，凡在本发明的技术方案的基础之上，所做的任何修改、等同替换、改进等，均应包括在本发明的保护范围之内。

Claims (30)

  1. 一种生物信号采集方法，其特征在于，包括：
    获取至少一个运动传感器的输出数据；
    至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；
    在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
  2. 如权利要求1所述的方法，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长，按照所述第一时长采集所述用户的至少一种生物信号，当所述第一时长到时，停止采集所述至少一种生物信号。
  3. 如权利要求1所述的方法，其特征在于，所述至少一种生物信号具有周期性。
  4. 如权利要求3所述的方法，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一值，检测所述至少一种生物信号的特征参考点个数，当所述特征参考点个数等于所述第一值时，停止采集所述至少一种生物信号。
  5. 如权利要求2或4所述的方法，其特征在于，所述活动类型 包括跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉中的任一种。
  6. 如权利要求1至5任一所述的方法，其特征在于，所述运动传感器包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种。
  7. 一种生物信号采集装置，其特征在于，包括：
    获取单元，用于获取至少一个运动传感器的输出数据；
    控制单元，用于至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；
    采集单元，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
  8. 如权利要求7所述的装置，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长，按照所述第一时长采集所述用户的至少一种生物信号，当所述第一时长到时，停止采集所述至少一种生物信号。
  9. 如权利要求7所述的装置，其特征在于，所述至少一种生物信号具有周期性。
  10. 如权利要求9所述的装置，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一值，检测所述至少一种生物信号的特征参考点个数，当所述特征参考点个数等于所述第一值时，停止采集所述至少一种生物信号。
  11. 如权利要求8或10所述的装置，其特征在于，所述活动类型包括跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉中的任一种。
  12. 如权利要求7至11所述的装置，其特征在于，所述运动传感器包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种。
  13. 一种电子设备，其特征在于，包括：
    至少一个运动传感器，存储器，处理器，和至少一个生物传感器；
    所述至少一个运动传感器，用于监测用户的运动；
    所述存储器，用于存储指令或数据；
    所述处理器与所述存储器耦合，所述处理器用于：获取所述至少一个运动传感器的输出数据；并至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；
    所述至少一个生物传感器，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
  14. 如权利要求13所述的电子设备，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长，按照所述第一时长采集所述用户的至少一种生物信号，当所述第一时长到时，停止采集所述至少一种生物信号。
  15. 如权利要求13所述的电子设备，其特征在于，所述至少一种生物信号具有周期性。
  16. 如权利要求15所述的电子设备，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动 类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一值，检测所述至少一种生物信号的特征参考点个数，当所述特征参考点个数等于所述第一值时，停止采集所述至少一种生物信号。
  17. 如权利要求14或16所述的电子设备，其特征在于，所述活动类型包括跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉中的任一种。
  18. 如权利要求13至17任一所述的电子设备，其特征在于，所述运动传感器包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种。
  19. 一种生物信号采集系统，其特征在于，包括：
    至少一个运动传感器，存储器，处理器，和至少一个生物传感器；
    所述至少一个运动传感器，用于监测用户的运动；
    所述存储器，用于存储指令或数据；
    所述处理器与所述存储器耦合，所述处理器用于：获取所述至少一个运动传感器的输出数据；并至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长；
    所述至少一个生物传感器，用于在所述至少一种生物信号的采集时长内采集所述用户的所述至少一种生物信号。
  20. 如权利要求19所述的系统，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一时长，按照所述第一时长采集所述用户的至少一种生物信号，当所述第一时长到时，停止采集所述至少一种生物信号。
  21. 如权利要求20所述的系统，其特征在于，所述至少一种生物信号具有周期性。
  22. 如权利要求21所述的系统，其特征在于，所述至少根据所述至少一个运动传感器的输出数据控制用户的至少一种生物信号的采集时长，包括：
    至少根据所述至少一个运动传感器的输出数据识别用户的活动类型和活动强度中的至少一项；
    获取与所述用户的活动类型和活动强度中的至少一项相匹配的第一值，检测所述至少一种生物信号的特征参考点个数，当所述特征参考点个数等于所述第一值时，停止采集所述至少一种生物信号。
  23. 如权利要求20或22所述的系统，其特征在于，所述活动类型包括跑步、步行、骑自行车、游泳、登山、站立、静坐、睡觉中的任一种。
  24. 如权利要求19至23任一所述的系统，其特征在于，所述至少一个运动传感器与所述处理器通过无线接口耦合。
  25. 如权利要求19至23任一所述的系统，其特征在于，所述至少一个运动传感器与所述处理器通过有线接口耦合。
  26. 如权利要求19至25任一所述的系统，其特征在于，所述至少一个生物传感器与所述处理器通过无线接口耦合。
  27. 如权利要求19至25任一所述的系统，其特征在于，所述至少一个生物传感器与所述处理器通过有线接口耦合。
  28. 如权利要求19至27任一所述的系统，其特征在于，所述至少一个运动传感器与所述处理器设置于同一设备中，或分别设置于不同的设备中。
  29. 如权利要求19至28任一所述的系统，其特征在于，所述至少一个生物传感器与所述处理器设置于同一设备中，或分别设置于不同的设备中。
  30. 如权利要求19至29任一所述的系统，其特征在于，所述运动传感器包括加速度计、陀螺仪、压力传感器、麦克风、磁力计和高度计中的任一种。
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