WO2024103547A1 - 一种可穿戴设备 - Google Patents

一种可穿戴设备 Download PDF

Info

Publication number
WO2024103547A1
WO2024103547A1 PCT/CN2023/075666 CN2023075666W WO2024103547A1 WO 2024103547 A1 WO2024103547 A1 WO 2024103547A1 CN 2023075666 W CN2023075666 W CN 2023075666W WO 2024103547 A1 WO2024103547 A1 WO 2024103547A1
Authority
WO
WIPO (PCT)
Prior art keywords
human body
electrode
state
heart rate
rate variability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2023/075666
Other languages
English (en)
French (fr)
Inventor
周鑫
刘嘉
黎美琪
苏雷
张宇翔
廖风云
齐心
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Shokz Co Ltd
Original Assignee
Shenzhen Shokz Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Shokz Co Ltd filed Critical Shenzhen Shokz Co Ltd
Priority to CN202380030543.5A priority Critical patent/CN118973475A/zh
Priority to EP23889996.7A priority patent/EP4509043A4/en
Priority to CN202321404344.XU priority patent/CN220778334U/zh
Publication of WO2024103547A1 publication Critical patent/WO2024103547A1/zh
Priority to US18/936,984 priority patent/US20250057461A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • 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
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/0245—Measuring pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • 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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • 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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring 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 or 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
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • 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
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/251—Means for maintaining electrode contact with the body
    • A61B5/256—Wearable electrodes, e.g. having straps or bands
    • 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
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
    • A61B5/282—Holders for multiple electrodes
    • 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]
    • A61B5/346—Analysis of electrocardiograms
    • 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]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • 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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • A61B5/6807—Footwear
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6823—Trunk, e.g., chest, back, abdomen, hip
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6831—Straps, bands or harnesses
    • 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
    • A61B5/7203—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
    • A61B5/7214—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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 for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02438—Measuring pulse rate or heart rate with portable devices, e.g. worn by the patient

Definitions

  • the present application relates to the field of wearable devices, and in particular to a wearable device capable of determining a body state.
  • One of the embodiments of the present specification provides a wearable device, comprising: at least two electrodes, configured to fit human skin to collect electrocardiogram signals of the human body; a wearable structure, configured to carry the at least two electrodes and fit the at least two electrodes to both sides of the midsagittal plane of the human body; and a processor, determining the heart rate variability of the human body based on the electrocardiogram signals, and determining the physical state of the human body at least based on the heart rate variability.
  • FIG1 is a diagram of an exemplary application scenario of a wearable device according to some embodiments of this specification.
  • FIG2 is an exemplary structural diagram of a wearable device according to some embodiments of this specification.
  • FIG3 is a schematic plan view of a wearable device according to some embodiments of the present specification.
  • FIG4 is a schematic diagram of a structure of a wearable device according to some embodiments of this specification.
  • FIG5 is a schematic diagram of a structure of a wearable device according to some embodiments of this specification.
  • Fig. 6 is a schematic diagram of a human body
  • Fig. 7 is a schematic diagram of the ilium in the waist region of a human body
  • FIG8 is a schematic diagram of a structure of a wearable device according to some embodiments of this specification.
  • FIG9 is a schematic diagram of the front structure of trousers according to some embodiments of the present specification.
  • FIG10 is a schematic diagram of the back structure of trousers according to some embodiments of the present specification.
  • FIG11 is a standard electrocardiogram according to some embodiments of the present specification.
  • FIG12 is a schematic diagram of determining a human body's physical state based on heart rate variability according to some embodiments of this specification.
  • FIG13 is a schematic diagram of determining a human body's physical state based on heart rate variability and heart rate according to some embodiments of this specification;
  • FIG. 14 is a schematic diagram of determining a human body state based on heart rate variability and respiratory state information according to some embodiments of this specification.
  • FIG. 15 is an exemplary structural diagram of another wearable device according to some embodiments of the present specification.
  • the embodiment of the present specification provides a wearable device, comprising: at least two electrodes, configured to fit the human skin to collect the electrocardiogram signal of the human body; a wearable structure, configured to carry the at least two electrodes, and fit the at least two electrodes to the waist area on both sides of the midsagittal plane of the human body; and a processor, which determines the heart rate variability of the human body based on the electrocardiogram signal, and determines the body condition of the human body at least based on the heart rate variability. State.
  • the wearable device provided in this specification can determine the physical state of a person by real-time detection of parameters such as the heart rate variability (HRV) and heart rate, respiratory state information, etc., and provide real-time feedback of the physical state and reference health advice to the user, thus realizing a closed loop of the user's health monitoring process; in addition, the user's physical state can include tension state, relaxation state, etc., and the determination of the physical state can be applied to various fields such as physical exercise, psychological testing, and clinical health monitoring.
  • HRV heart rate variability
  • FIG. 1 is a diagram of an exemplary application scenario of a wearable device according to some embodiments of this specification.
  • the application scenario 100 may include a processing device 110, a network 120, a wearable device 130, a terminal device 140, and a feedback module 150.
  • the application scenario 100 may be applied to various scenarios such as physical exercise, psychological testing, and clinical health monitoring.
  • the application scenario 100 can be used to measure the user's physical state during exercise.
  • Exemplary exercises may include yoga, Pilates, meditation, sitting meditation, standing meditation, breathing training, etc.
  • Exemplary physical states may include a relaxed state and a tense state.
  • the processing device 110 may include a processor. In some embodiments, the processing device 110 may be used to process electrical signals generated by the wearable device 130 and the terminal device 140. For example, the processing device 110 may receive an electrocardiogram signal transmitted by the wearable device 130 through the network 120; the processing device 110 may receive input information input by the user through the terminal device 140 through the network 120. In some embodiments, the processing device 110 may determine the heart rate variability of the human body based on the electrocardiogram signal, and determine the physical state of the human body at least according to the heart rate variability. In some embodiments, the processing device 110 may determine the heart rate variability and heart rate of the human body based on the electrocardiogram signal, and determine the physical state of the human body according to the heart rate variability and heart rate.
  • the processing device 110 may complete the training of the machine learning model, at least with the heart rate variability as input data, and the machine learning model outputs the physical state of the human body based on the input data.
  • the processing device 110 may determine a calibration curve, and determine the physical state of the human body according to the calibration curve.
  • the processing device 110 may determine the physical state of the human body according to the heart rate variability and respiratory state information.
  • the processing device 110 can use at least heart rate variability and respiratory state information as input data through a machine learning model that has completed training, and the machine learning model outputs the body state of the human body based on the input data.
  • the processing device 110 can identify the movement of the corresponding part of the human body based on the second strain sensor, where the movement of the corresponding part is related to the position of the second strain sensor. For example, when the second strain sensor is located on the leg or buttocks of the human body, the processing device 110 can identify the movement of the lower limbs of the human body based on the second strain sensor. In some embodiments, the processing device 110 can issue a control instruction in response to the body state of the human body.
  • the processing device 110 may be local or remote.
  • the processing device 110 may access information stored in the wearable device 130 and/or the terminal device 140 directly or through the network 120.
  • the processing device 110 may be directly connected to the wearable device 130 and/or the terminal device 140 to access the information stored therein.
  • the processing device 110 may be located in the wearable device 130 and realize information interaction with the terminal device 140 through the network 120.
  • the processing device 110 may be located in the terminal device 140 and realize information interaction with the wearable device 130 through the network 120.
  • the processing device 110 may be executed on a cloud platform.
  • the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, etc.
  • the processing device 110 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-core processing device).
  • the processing device 110 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc., or any combination thereof.
  • CPU central processing unit
  • ASIC application-specific integrated circuit
  • ASIP application-specific instruction processor
  • GPU graphics processing unit
  • PPU physical processing unit
  • DSP digital signal processor
  • FPGA field programmable gate array
  • PLD programmable logic device
  • controller a controller
  • microcontroller unit a reduced instruction set computer (RISC)
  • RISC reduced instruction set
  • the network 120 can facilitate the exchange of data and/or information between the components in the application scenario 100.
  • one or more components in the application scenario 100 e.g., the processing device 110, the wearable device 130, the terminal device 140, the feedback module 150
  • the electrical signal generated by the terminal device 140 can be transmitted to the processing device 110 through the network 120.
  • the network 120 can be any type of wired or wireless network.
  • the network 120 can include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, an internet network, a local area network (LAN), a wide area network (WAN), a wireless area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, etc. or any combination thereof.
  • the network 120 may include one or more network entry and exit points.
  • the network 120 may include wired or wireless network access points, such as base stations and/or Internet exchange points 120-1, 120-2, ..., through which one or more components of the application scenario 100 may be connected to the network 120 to exchange data and/or information.
  • Wearable device 130 refers to clothing or equipment with wearable function.
  • wearable device 130 may include but is not limited to pants, belts, etc.
  • Wearable device 130 can be used to obtain the user's electrocardiogram signal and electromyography signal, and transmit the above signals to processing device 110 through network 120.
  • wearable device 130 can be provided with at least two electrodes, which are configured to fit human skin to collect human electrocardiogram signals.
  • wearable device 130 also includes a first strain sensor, which collects human breathing state information based on the fluctuation of the waist area when the human body breathes.
  • wearable device 130 also includes a second strain sensor, which can be located at the user's legs, buttocks, arms, shoulders, hands, etc.
  • the wearable device 130 may further include an electromyographic module, which is used to collect electromyographic signals of the human body.
  • wearable device 130 is not limited to the pants shown in Figure 1, but may also include other devices, such as belts, skirts, wrist guards, elbow pads, shoulder pads, knee pads, socks, etc., which are not limited here. Any device that can use this manual is within the scope of protection of this manual.
  • the terminal device 140 may be a device that interacts with the user. In some embodiments, the terminal device 140 may be integrated into the wearable device 130. The terminal device 140 may receive input information input by the user and send the input information to the processing device 110 through the network 120. In some embodiments, the terminal device 140 may receive feedback information sent by the processing device 110 through the network 120 and present the feedback information to the user. Exemplary feedback information may include information about the current physical state, exercise suggestions, health reminders, and the like. In some embodiments, the terminal device 140 may be a mobile smart terminal 141, a tablet computer 142, a laptop computer 143, and the like. In some embodiments, the terminal device 140 may be integrated into the processing device 110 and the wearable device 130.
  • the terminal device 140 may be other devices.
  • a mobile phone a smart home device, a smart action device, a virtual reality device, an augmented reality device, and the like, or any combination thereof.
  • the smart home device may include a control device for a smart appliance, a smart monitoring device, a smart TV, a smart camera, and the like, or any combination thereof.
  • the smart action device may include a smart phone, a personal digital assistant (PDA), a game device, a navigation device, a POS device, and the like, or any combination thereof.
  • the virtual reality device and/or augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality goggles, an augmented reality helmet, augmented reality glasses, augmented reality goggles, etc., or any combination thereof.
  • the feedback module 150 can be used to send feedback information to the user based on the control instruction.
  • the feedback module 150 can be a speaker, an electronic screen, a vibration sensing device, and other devices.
  • the feedback module 150 can be a part of the terminal device 140, or integrated into the terminal device 140.
  • the feedback module 150 can also be integrated on the wearable device 130 and set independently relative to the terminal device 140.
  • the application scenario 100 may also include other devices or components, such as a database.
  • the database can store data, for example, physiological parameter information (such as electrocardiogram signals, electromyography signals, heart rate, heart rate variability, respiratory status information, etc.) received by the terminal device 140.
  • physiological parameter information such as electrocardiogram signals, electromyography signals, heart rate, heart rate variability, respiratory status information, etc.
  • the database can store information obtained from the wearable device 130 and/or the mobile terminal device.
  • the database may include a large-capacity memory, a removable memory, a volatile read-write memory (for example, a random access memory RAM), a read-only memory (ROM), etc., or any combination thereof.
  • the database can be connected to the network 120 to communicate with one or more components of the application scenario 100 (for example, the processing device 110, the wearable device 130, the terminal device 140, the mobile terminal device, etc.).
  • One or more components of the application scenario 100 can access the data stored in the database through the network 120.
  • the database can be part of the processing device 110.
  • FIG. 2 is an exemplary structural diagram of a wearable device according to some embodiments of the present specification.
  • the wearable device 200 may include at least two electrodes 210, a wearable structure 220, and a processor 230.
  • the at least two electrodes 210 may collect the electrocardiogram signal of the human body, and the processor 230 may process the electrocardiogram signal into information such as heart rate and heart rate variability, and determine the physical state of the human body through the above information.
  • At least two electrodes 210 may be configured to fit human skin to collect ECG signals of the human body.
  • the number of electrodes 210 may be 2, 3 or other numbers.
  • at least two electrodes 210 may be located on both sides of the median sagittal plane of the human body.
  • at least two electrodes 210 may be located on the ilium, lower back or abdomen or legs on both sides of the median sagittal plane of the human body to collect ECG signals of the human body.
  • the electrode 210 may be fixed to the wearable structure 220 by means of Velcro, mesh bag, buckle, hot pressing, gluing, suturing, etc.
  • the electrode 210 may include a first electrode and a second electrode, and the first electrode and the second electrode may be located on both sides of the median sagittal plane of the human body, respectively.
  • the electrode 210 see FIG. 3 and its related description.
  • the wearable structure 220 can be configured to carry at least two electrodes 210, and fit at least two electrodes 210 to the two side areas of the midsagittal plane of the human body.
  • the wearable structure 220 can be a comfortable and breathable fabric, such as cotton, linen, nylon and other fabrics.
  • the wearable structure 220 can be one or a combination of trousers (such as shorts, trousers, jumpsuits), a belt, a skirt, a knee pad, and a waist guard.
  • trousers such as shorts, trousers, jumpsuits
  • the processor 230 may be configured to determine the heart rate variability of the human body based on the electrocardiogram signal, and determine the physical state of the human body at least based on the heart rate variability.
  • the processor 230 may be integrated at any position on the wearable structure 220, or may be independently set relative to the wearable structure 220.
  • the processor 230 may be set in a cloud server.
  • the processor 230 may determine the heart rate variability of the human body based on the electrocardiogram signal, and determine the physical state of the human body at least based on the heart rate variability.
  • the processor 230 may determine the heart rate variability and heart rate of the human body based on the electrocardiogram signal, and determine the physical state of the human body based on the heart rate variability and heart rate.
  • the processor 230 may complete the training of a machine learning model, at least using the heart rate variability as input data, and the machine learning model outputs the physical state of the human body based on the input data.
  • the processor 230 may determine a calibration curve, and determine the physical state of the human body based on the calibration curve.
  • the processor 230 may determine the physical state of the human body based on the heart rate variability and respiratory state information.
  • the processor 230 can use at least the heart rate variability and the respiratory state information as input data by completing the trained machine learning model, and the machine learning model outputs the physical state of the human body based on the input data.
  • the processor 230 can identify the movement of the lower limbs of the human body based on the second strain sensor. In some embodiments, the processor 230 can issue a control instruction in response to the physical state of the human body. For a specific description of the processor 230, see Figures 11 to 16 and their related descriptions.
  • Heart rate variability can reflect the changes in the time interval between each heart beat.
  • a healthy heart is irregular, and the irregularity of the heart is controlled by the autonomic nervous system.
  • Heart rate variability can reflect the health of the nervous system.
  • There are two types of autonomic nerves one is the sympathetic nerve that controls "fight or escape", and the other is the parasympathetic nerve that controls "relaxation or digestion”.
  • the heart rate variability When under the control of the sympathetic nerves, the heart rate variability will decrease, while under the control of the parasympathetic nerves, the heart rate variability will increase.
  • the time interval between each heart beat varies greatly.
  • the time interval between each heart beat is small, and it can even achieve regular beating with equal time intervals.
  • the analysis methods of heart rate variability may include linear analysis and nonlinear analysis.
  • the linear analysis method may include time domain analysis and frequency domain analysis.
  • the time domain analysis further includes statistical analysis and geometric analysis.
  • the nonlinear analysis method may include image method and nonlinear parameter calculation method.
  • the image method may include electrocardiogram scatter plot method.
  • the nonlinear analysis method may include fractal dimension (correlation dimension, Hausdorf dimension or information dimension) analysis method, complexity analysis method, Lyapaunov index, Kolmogorov entropy. Approximate entropy analysis, etc.
  • the study of heart rate variability can better reflect the changes in some physiological data during the interaction between wearable devices and the human body.
  • Fig. 3 is a schematic plan view of a wearable device according to some embodiments of the present specification.
  • Fig. 4 and Fig. 5 are schematic structural views of a wearable device according to some embodiments of the present specification.
  • the wearable device 300 may include a first electrode 311 , a second electrode 312 , and a wearable structure 320 .
  • the first electrode 311 and the second electrode 312 are used to attach to human skin and collect the ECG signal of the human body.
  • the first electrode 311 and the second electrode 312 can be attached to different parts of the human body, wherein the part attached to the first electrode 311 can have a first potential, and the part attached to the second electrode 312 can have a second potential, and the difference between the first potential and the second potential can be used to reflect the ECG signal of the human body.
  • the wearable structure 320 is used to carry the first electrode 311 and the second electrode 312, and the first electrode 311 and the second electrode 312 are attached to both sides of the median sagittal plane of the human body.
  • the first electrode 311 and the second electrode 312 can be distributed on the wearable structure 320 at intervals.
  • the first electrode 311 and the second electrode 312 can be respectively located on both sides of the median sagittal plane of the human body, respectively detecting the potentials (for example, the first potential and the second potential) on both sides of the median sagittal plane of the human body, and determining the electrocardiogram signal according to the difference between the potentials on both sides of the median sagittal plane of the human body.
  • the wearable structure 320 is worn on the waist area of the human body, so that the first electrode 311 and the second electrode 312 are respectively located on both sides of the median sagittal plane of the human body, that is, the first electrode 311 and the second electrode 312 are respectively located on both sides of the body, and the difference between the potentials on both sides of the median sagittal plane of the human body can reflect the strength of the electrocardiogram signal.
  • the strength of the electrocardiogram signal can be increased.
  • the wearable structure 320 may have a first extension direction and a second extension direction perpendicular to the first extension direction, and the first electrode 311 and the second electrode 312 may be spaced apart along the first extension direction of the wearable structure.
  • the first extension direction of the wearable structure 320 may refer to the circumferential direction of the wearable structure 320 worn on the waist region of the human body when the wearable structure 320 is worn.
  • the wearable structure 320 may be an elastic belt-like structure, and the first electrode 311 and the second electrode 312 may be spaced apart on the belt-like structure along the extension direction (or length direction) of the belt-like structure.
  • the user may fasten the belt-like structure to the waist region of the user by tying the belt-like structure or fixing it by a buckle, so that the first electrode 311 and the second electrode 312 may be located on both sides of the midsagittal plane of the human body, respectively.
  • the first extension direction of the wearable structure 320 is the extension direction of the belt-like structure.
  • the wearable structure 320 may be in the form of a waist belt shown in FIG.
  • the first electrode 311 and the second electrode 312 may be spaced apart and arranged on the surface where the waist belt fits the human skin.
  • the first extension direction of the wearable structure 320 may be the length direction of the waist belt after it is unfolded.
  • the two ends of the waist belt may be combined by means of Velcro or buckles, etc., so as to facilitate the user to wear or take it off.
  • the wearable structure 320 is an elastic belt-like structure, which can ensure that the user has good wearing comfort and is washable.
  • the wearable structure 320 may also be in the form of trousers (e.g., shorts, trousers, jumpsuits, etc.) shown in FIG.
  • the first electrode 311 and the second electrode 312 may be arranged at intervals along the circumference of the waist of the trousers and arranged on the inside of the waist.
  • the first electrode 311 and the second electrode 312 at the waist may be located on both sides of the midsagittal plane of the human body, respectively.
  • the first electrode 311 and the second electrode 312 may be located at the left waist and the right waist, the left hip and the right hip, the left knee joint and the right knee joint, the left ankle and the right ankle, etc. (as shown in the dotted rectangular position in FIG. 5 ).
  • the wearable structure 320 may also be in the form of a skirt, and the first electrode 311 and the second electrode 312 may be arranged at intervals along the circumference of the waist of the skirt and arranged on the inside of the waist, so that the first electrode 311 and the second electrode 312 may be located on both sides of the midsagittal plane of the human body, respectively.
  • the first extension direction of the wearable structure 320 may be the length direction of the trousers waist or the skirt waist after it is unfolded along its circumference.
  • the first electrode 311 and the second electrode 312 in the wearable device 300 can also be detachably connected to the wearable structure 320.
  • the wearable structure 320 is a belt-shaped structure or trousers, and the side of the first electrode 311 and the second electrode 312 that does not contact the human skin can be connected to the wearable structure 320 by bonding, snapping, embedding, etc.
  • the first electrode 311 and the second electrode 312 are installed on the waist, the end of the trouser leg, the middle of the trouser leg, and the buttocks of the belt-shaped structure or trousers.
  • the first electrode 311 and the second electrode 312 can be adjusted according to the user's own body shape (for example, height, weight, waist circumference, etc.). 320 to suit users of different body shapes.
  • the wearable structure 320 for example, a belt-like structure or trousers
  • the first electrode 311, the second electrode 312 and other components can be removed from the wearable structure 320 to prevent the wearable device 300 from damaging other components such as the first electrode 311 and the second electrode 312 during the cleaning process.
  • a first Velcro can be provided on the side of the wearable structure 320 (for example, a belt-like structure or the waist of trousers) that fits the user, and the first Velcro can be distributed along the extension direction of the belt-like structure or the circumference of the waist.
  • the first electrode 311 and the second electrode 312 are provided on the side opposite to the user's body contact side.
  • the second Velcro can be bonded to the first Velcro to achieve a detachable connection between the first electrode 311, the second electrode 312 and the wearable structure 320.
  • the first Velcro can cover the side of the wearable structure 320 that fits the user.
  • the number of first Velcro can be multiple, and multiple first Velcros are sequentially spliced on the side of the wearable structure 320 that fits the user. At this time, the gaps between the multiple first Velcros can enable the wearable structure 320 to maintain its own elasticity, and it is also convenient to adjust the position of the first electrode 311 and the second electrode 312 in the wearable structure 320, so as to be suitable for users of different body shapes.
  • multiple first Velcros can also be distributed and spaced along the extension direction of the band structure or the peripheral side of the electrode position.
  • each first Velcro can be 4cm, and the spacing between two adjacent first Velcros is 1cm.
  • the first Velcro or the second Velcro can be made of non-woven fabric material, which is relatively soft.
  • the wearable device 300 may include multiple groups of electrode modules (for example, a first electrode 311 and a second electrode 312), each group of electrode modules is provided with a wire, one end of the wire is connected to the first electrode 311 or the second electrode 312 in the electrode module, and the other end of the wire is provided with an interface plug-in, and the interface plug-in can be electrically connected to the circuit structure of the wearable device 300, for example, a port adapted to the interface is provided on the circuit board of the circuit structure.
  • the user can replace different electrode modules according to their own situation.
  • the sizes of multiple electrode modules may be the same or different.
  • the size of the first electrode or the second electrode in the electrode module may be 1cm*1cm, 1cm*2cm, 1cm*3cm, 1cm*4cm, 1cm*5cm, 2cm*2cm, 2cm*3cm, 2cm*4cm, 2cm*5cm, 3cm*3cm, 3cm*4cm, 3cm*5cm, 4cm*4cm, 4cm*5cm or 5cm*5cm. It should be noted that the first electrode or the second electrode in the electrode module is not limited to the sizes listed above, and may also be other sizes.
  • the first electrode 311 or the second electrode 312 can be attached to the human skin in the form of a flexible patch, and the patch can be a regular shape such as a circle, an ellipse, a rectangle, a diamond or other irregular shapes.
  • the shape of the first electrode 311 or the second electrode 312 can be designed according to the shape of the bones under the skin of the waist area where the first electrode 311 or the second electrode 312 is attached.
  • the first electrode 311 or the second electrode 312 can be an electrode made of a single material, such as a metal fabric electrode, a conductive silicon electrode, a hydrogel electrode, a metal electrode, etc.
  • the first electrode 311 or the second electrode 312 can be a metal fabric electrode and a conductive silicon electrode. Further preferably, the first electrode 311 or the second electrode 312 can be a metal fabric electrode, and the metal fabric electrode has a smaller resistivity, and its impedance and the contact impedance between the skin are also smaller. The smaller the contact impedance between the first electrode 311 or the second electrode 312 and the skin, the more conducive it is to reduce the interference of motion artifacts on the electrocardiogram signals collected by the first electrode 311 and the second electrode 312.
  • the first electrode 311 or the second electrode 312 may protrude from the surface of the wearable structure 320 around it, so that a pre-pressure can be provided for the first electrode 311 or the second electrode 312, which is conducive to the first electrode 311 or the second electrode 312 fully fitting with the human skin to accurately collect ECG signals.
  • the height of the first electrode 311 or the second electrode 312 protruding relative to the surface of the wearable structure 320 around it depends on the thickness of the first electrode 311 or the second electrode 312. Among them, the thickness of the first electrode 311 or the second electrode 312 may refer to the size of the first electrode 311 or the second electrode 312 in a direction perpendicular to the surface of the wearable structure 320 around it.
  • the thickness of the first electrode 311 or the second electrode 312 is related to its material.
  • the thickness of the metal fabric electrode when using a metal fabric electrode to collect ECG signals, the thickness of the metal fabric electrode may be 10 ⁇ m to 5mm.
  • the thickness of the metal fabric electrode may be 100 ⁇ m to 3mm.
  • the thickness of the metal fabric electrode may be 500 ⁇ m to 2mm.
  • the first electrode 311 or the second electrode 312 can also be an electrode formed by superimposing different materials, such as an electrode composed of a metal fabric material and a conductive silicon material. Not only is the contact impedance between the electrode and the skin small, but the conductive silicon in contact with the skin has the advantages of skin-friendliness, washing resistance, and friction resistance, thereby avoiding discomfort to the human body caused by the contact between the electrode and the skin.
  • the size of the first electrode 311 or the second electrode 312 in the first extension direction or the size of the second extension direction should be as large as possible, provided that the size of the first electrode 311 or the second electrode 312 in the first extension direction or the size of the second extension direction does not exceed the size of the wearable structure 320 in the first extension direction or the size of the second extension direction, so as to ensure that the first electrode 311 or the second electrode 312 has a larger contact area with the skin, so as to reduce the contact impedance between the first electrode 311 or the second electrode 312 and the contacted skin, thereby reducing the interference of motion artifacts on the ECG signal.
  • the first electrode 311 or the second electrode 312 having a larger size in the first extension direction or the size in the second extension direction can make the first electrode 311 or the second electrode 312 less susceptible to deformation and displacement during wearing, which is conducive to reducing motion artifacts and improving the quality of ECG signals.
  • the first electrode 311 or the second electrode 312 having a larger size in the first extension direction or the size in the second extension direction can ensure that the first electrode 311 or the second electrode 312 will not completely fall off the skin in various movements or wrinkles, thereby affecting the collection of ECG signals.
  • the size of the first electrode 311 or the second electrode 312 in the first extension direction or the size in the second extension direction may refer to the maximum size of the first electrode 311 or the second electrode 312 in the first extension direction or the maximum size in the second extension direction, respectively.
  • the size of the first electrode 311 or the second electrode 312 in the first extension direction may be in the range of 5 mm to 50 mm
  • the size of the first electrode 311 or the second electrode 312 in the second extension direction may be in the range of 5 mm to 50 mm. It can be in the range of 5mm to 50mm.
  • the size of the first electrode 311 or the second electrode 312 in the first extension direction can be in the range of 10mm to 45mm, and the size of the first electrode 311 or the second electrode 312 in the second extension direction can be in the range of 10mm to 45mm. In some embodiments, the size of the first electrode 311 or the second electrode 312 in the first extension direction can be in the range of 15mm to 40mm, and the size of the first electrode 311 or the second electrode 312 in the second extension direction can be in the range of 15mm to 40mm.
  • the size of the first electrode 311 or the second electrode 312 in the first extension direction can be in the range of 20mm to 30mm, and the size of the first electrode 311 or the second electrode 312 in the second extension direction can be in the range of 20mm to 30mm.
  • an isolation layer may be provided between the first electrode 311 or the second electrode 312 and the surface of the wearable structure 320 close to the human skin. Specifically, the isolation layer is located on the surface of the wearable structure 320 around the first electrode 311 or the second electrode 312. The isolation layer can prevent the wearable structure 320 from accidentally connecting the first electrode 311 or the second electrode 312 when it is soaked, resulting in a weak or inaccurate collected ECG signal.
  • the isolation layer may be made of an insulating and waterproof material.
  • the material of the isolation layer may include rubber, a high molecular polymer, silicone, etc., or any combination thereof.
  • the wearable device 300 provided in the embodiment of this specification can be worn in the waist area, hip area, knee joint area, ankle area, etc. of the human body, so that the first electrode 311 and the second electrode 312 are respectively located on both sides of the median sagittal plane of the human body, and fit with the skin of the waist area on both sides of the median sagittal plane of the human body to collect the electrocardiogram signal of the human body, so that the heart condition of the user wearing the wearable device 300 can be monitored, and the user can also be ensured to have good wearing comfort.
  • the fitting position of the first electrode 311 and the second electrode 312 in the waist area on both sides of the median sagittal plane of the human body is related to the quality and strength of the collected electrocardiogram signal. The fitting position of the first electrode 311 and the second electrode 312 to the human skin will be described in detail below in combination with a specific human body schematic diagram.
  • Fig. 6 is a schematic diagram of the human body.
  • Fig. 6 (a) shows the front of the human body
  • Fig. 6 (b) shows the back of the human body.
  • Fig. 7 is a schematic diagram of the ilium in the waist area of the human body.
  • Fig. 7 (a) shows the front of the ilium
  • Fig. 7 (b) shows the back of the ilium.
  • the median sagittal plane of the human body refers to a plane passing through the median line 601 of the human body and dividing the human body into two equal or similar parts.
  • the median line 601 of the human body can be determined according to the line from the tip of the nose to the middle of the two nipples of the human body, the line from the middle of the two nipples to the middle of the umbilicus of the abdomen, or the line from the middle of the umbilicus of the abdomen to the middle of the pubic symphysis joint.
  • the waist area of the human body can be the area between the lower end of the ribs of the human body and the lower end of the ilium, which mainly includes the abdomen and the ilium.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the skin of the waist area on both sides of the median sagittal plane of the human body.
  • the median sagittal plane of the human body can divide the waist area of the human body into a left waist area and a right waist area.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the skin of the left waist area and the right waist area.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the skin of the left waist area and the right waist area. In some embodiments, the first electrode 311 and the second electrode 312 can be respectively attached to the skin of the left buttocks and the right buttocks. In some embodiments, the first electrode 311 and the second electrode 312 can be attached to the skin of the left knee joint and the right knee joint, respectively. In some embodiments, the first electrode 311 and the second electrode 312 can be attached to the skin of the left ankle and the right ankle, respectively.
  • first electrode 311 and the second electrode 312 can also be attached to other parts, such as attached to the skin of the left thigh and the right thigh, the skin of the left calf and the right calf, etc.
  • first electrode 311 and the second electrode 312 can be symmetrically arranged about the midsagittal plane of the human body, so that the motion artifacts at the positions where the first electrode 311 and the second electrode 312 are attached have good consistency, which is conducive to the elimination of motion artifacts.
  • the motion artifacts in the electrocardiogram signal can be eliminated by a differential amplifier circuit to improve the quality of the electrocardiogram signal.
  • the consistency between the motion artifacts at the positions where the first electrode 311 and the second electrode 312 are attached can be further improved, which is more conducive to the elimination of motion artifacts and makes the electrocardiogram signal have higher quality.
  • the consistency of the first electrode 311 and the second electrode 312 may include consistency in material, size (for example, size in the first extension direction, size in the second extension direction and thickness, etc.), pressure on the skin, etc., or a combination thereof.
  • the motion artifacts of the positions where the first electrode 311 and the second electrode 312 are attached can also be made consistent by making the wearable structure 320 carry the first electrode 311 and the second electrode 312 partially elastic, so as to eliminate the motion artifacts and improve the quality of the ECG signal.
  • the first electrode 311 and the second electrode 312 can also be set asymmetrically about the median sagittal plane of the human body. For example, when the user wears the wearable device, the first electrode 311 is located on the abdomen of the human body on one side of the median sagittal plane, and the second electrode 312 is located at the ilium of the human body on the other side of the median sagittal plane.
  • the first electrode 311 when the user wears the wearable device, the first electrode 311 is located on the abdomen of the human body on one side of the median sagittal plane, and the second electrode 312 is located at the back waist of the human body on the other side of the median sagittal plane.
  • the materials, sizes, pressure on the skin, etc. of the first electrode 311 and the second electrode 312 may be consistent or inconsistent.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the ilium 701 positions on both sides of the median sagittal plane of the human body.
  • the median sagittal plane of the human body can divide the ilium 701 into a left ilium 7011 and a right ilium 7012, and the first electrode 311 and the second electrode 312 can be respectively attached to the skin areas corresponding to the left ilium 7011 and the right ilium 7012 to collect the electrocardiogram signal of the human body.
  • the attachment positions of the first electrode 311 and the second electrode 312 to the skin areas respectively covering the left ilium 7011 and the right ilium 7012 can be symmetrical about the median sagittal plane of the human body, so that the consistency of the motion artifacts of the attachment positions of the first electrode 311 and the second electrode 312 can be improved, thereby facilitating the elimination of motion artifacts and improving the quality of the electrocardiogram signal.
  • the positions where the first electrode 311 and the second electrode 312 fit the skin areas covering the left ilium 7011 and the right ilium 7012, respectively can also be related to the human body.
  • the midsagittal plane of the body is asymmetric.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the positions of the anterior superior iliac spine 702 on both sides of the midsagittal plane of the human body.
  • the anterior superior iliac spine 702 can include a left anterior superior iliac spine 7021 and a right anterior superior iliac spine 7022 located on the left ilium 7011 and the right ilium 7012, respectively.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the skin areas corresponding to the left anterior superior iliac spine 7021 and the right anterior superior iliac spine 7022 to collect the electrocardiogram signal of the human body. Since there are fewer muscles at the left anterior superior iliac spine 7021 and the right anterior superior iliac spine 7022, even when the human body is in motion, the electromyographic signal interferes less with the electrocardiogram signal collected by the first electrode 311 and the second electrode 312, which can ensure that the electrocardiogram signal has a good quality.
  • the left anterior superior iliac spine 7021 and the right anterior superior iliac spine 7022 are symmetrical about the median sagittal plane of the human body, and the motion artifacts corresponding to the first electrode 311 and the second electrode 312 have good consistency, which is conducive to eliminating the motion artifacts and improving the quality of the electrocardiogram signal.
  • the distance difference between the left anterior superior iliac spine 7021 and the right anterior superior iliac spine 7022 and the heart is large, and the potential difference between the two is also large.
  • the first electrode 311 and the second electrode 312 are respectively fitted with the skin areas corresponding to the left ilium 7011 and the right ilium 7012, which can improve the strength of the collected electrocardiogram signal.
  • left anterior superior iliac spine 7021 and the right anterior superior iliac spine 7022 have protrusions to fully fit with the first electrode 311 and the second electrode 312, ensuring that the first electrode 311 and the second electrode 312 are not easy to fall off, and the human body is sensitive to the skin areas corresponding to the left anterior superior iliac spine 7021 and the right anterior superior iliac spine 7022, which can ensure that the human body has a better wearing comfort when wearing the wearing structure 320.
  • the first electrode 311 and the second electrode 312 may be respectively attached to the posterior superior iliac spine 703 on both sides of the midsagittal plane of the human body.
  • the posterior superior iliac spine 703 may include a left posterior superior iliac spine 7031 located on the left ilium 7011 and a right posterior superior iliac spine 7032 located on the right ilium 7012.
  • the first electrode 311 and the second electrode 312 may be respectively attached to the skin areas corresponding to the left posterior superior iliac spine 7031 and the right posterior superior iliac spine 7032 to collect the electrocardiogram signal of the human body.
  • the electromyographic signal has less interference with the ECG signal collected by the first electrode 311 and the second electrode 312, thereby ensuring that the ECG signal has good quality.
  • the left posterior superior iliac spine 7031 and the right posterior superior iliac spine 7032 are symmetrical about the median sagittal plane of the human body, and the motion artifacts have good consistency, which is conducive to eliminating motion artifacts and improving the quality of the ECG signal.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the back waist 602 positions on both sides of the median sagittal plane of the human body.
  • the median sagittal plane of the human body divides the back waist 602 into the left back waist 6021 and the right back waist 6022.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the skin areas corresponding to the left back waist 6021 and the right back waist 6022 to collect ECG signals.
  • the attachment positions of the first electrode 311 and the second electrode 312 to the skin areas of the left back waist 6021 and the right back waist 6022 can be symmetrical about the median sagittal plane of the human body, so that the consistency of the motion artifacts of the attachment positions of the first electrode 311 and the second electrode 312 can be improved, thereby facilitating the elimination of motion artifacts and improving the quality of ECG signals.
  • the positions where the first electrode 311 and the second electrode 312 are attached to the skin areas of the left lower back 6021 and the right lower back 6022 respectively may be asymmetrical with respect to the median sagittal plane of the human body.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the abdomen 603 on both sides of the median sagittal plane of the human body.
  • the median sagittal plane of the human body divides the abdomen 603 into a left abdomen 6031 and a right abdomen 6032.
  • the first electrode 311 and the second electrode 312 can be respectively attached to the skin areas of the left abdomen 6031 and the right abdomen 6032 to collect ECG signals.
  • the attachment positions of the first electrode 311 and the second electrode 312 to the skin areas of the left abdomen 6031 and the right abdomen 6032 can be symmetrical about the median sagittal plane of the human body, which can improve the consistency of the motion artifacts of the attachment positions of the first electrode 311 and the second electrode 312, thereby facilitating the elimination of motion artifacts and improving the quality of ECG signals.
  • the positions where the first electrode 311 and the second electrode 312 are attached to the skin areas of the left abdomen 6031 and the right abdomen 6032 respectively may be asymmetrical with respect to the median sagittal plane of the human body.
  • FIG8 is a schematic diagram of the structure of a wearable device according to some embodiments of the present specification.
  • the wearable device 800 may include a first electrode 811, a second electrode 812, a reference electrode 813, and a wear structure 820.
  • the first electrode 811 and the second electrode 812 may be spaced apart on the surface of the wear structure 820 close to human skin, and the reference electrode 813 may be located between the first electrode 811 and the second electrode 812.
  • the first electrode 811, the second electrode 812, and the wear structure 820 are similar to the first electrode 311, the second electrode 312, and the wear structure 320 in the wearable device 300.
  • reference may be made to the related descriptions of the first electrode 311, the second electrode 312, and the wear structure 320, which will not be described in detail here.
  • the first electrode 811 and the second electrode 812 can be respectively attached to the sides of the midsagittal plane of the person, and the reference electrode 813 can be attached to the skin of the person.
  • the reference electrode 813 can provide a reference ground voltage for the first electrode 811 and the second electrode 812, so as to facilitate the subsequent processing of the first potential corresponding to the first electrode 811 and the second potential corresponding to the second electrode 812 by a circuit (e.g., a differential amplifier).
  • the reference electrode 813 can also be connected to the right leg drive The circuit is electrically connected, and the right leg drive circuit can reduce the impedance of the reference electrode 813 to the human body, so that the right leg drive circuit can more effectively reduce the power frequency interference.
  • the reference electrode 813 when the human body wears the wearable device 800, can be located at other positions such as the back waist side, abdomen, etc. of the waist area of the human body.
  • the first electrode 811 and the second electrode 812 are symmetrically arranged relative to the median sagittal plane of the human body
  • the reference electrode 813 can be located at the center of the back waist side of the waist area of the human body (for example, the lumbar spine and its vicinity), so that when the user wears the wearable device 800, each electrode is symmetrically arranged relative to the median sagittal plane of the human body, so that the value of the potential difference between the first potential corresponding to the first electrode 811 and the reference electrode 813 is approximately equal to the value of the potential difference between the second potential corresponding to the second electrode 812 and the reference electrode 813, so as to facilitate the processing of subsequent circuits (for example, differential amplifiers).
  • subsequent circuits for example, differential amplifiers
  • the number of reference electrodes 813 is not limited to the one shown in Figure 8, but can also be multiple.
  • the reference electrode 813 may include a first reference electrode and a second reference electrode.
  • the first reference electrode and the second reference electrode may be located at the left back waist and the right back waist of the human body's waist area, wherein the first reference electrode and the second reference electrode may be symmetrically arranged with respect to the median sagittal plane of the human body, so that when the human body wears the wearable device 800, each electrode on the wearable device 800 may be symmetrically arranged with respect to the median sagittal plane of the human body.
  • the first reference electrode and the second reference electrode may also be arranged asymmetrically with respect to the median sagittal plane of the human body.
  • the first reference electrode and the second reference electrode are both located at the left back waist or the right back waist of the human body.
  • the first reference electrode is located at a position on the left back waist of the human body away from the median sagittal plane of the human body
  • the second reference electrode is located at a position on the right back waist of the human body close to the median sagittal plane of the human body.
  • the first reference electrode and the second reference electrode are connected by a wire so that the two reference electrodes are at the same potential.
  • the first reference electrode and the second reference electrode can be used as a reference electrode, thereby increasing the degree of freedom of the wearable device 800 with respect to symmetry setting.
  • first reference electrode and the second reference electrode are not limited to being set at the back side of the waist area of the human body, but can also be located in other areas such as the abdomen, left and right waist sides, left and right buttocks, left and right thighs, etc.
  • the size of the reference electrode 813 along the second extension direction of the wearable structure 820 may be no less than the size of the first electrode 811 or the second electrode 812 along the second extension direction of the wearable structure 820, so that the reference electrode 813 fits the human body.
  • the size of the reference electrode 813 in the second extension direction may be in the range of 5 mm to 80 mm. In some embodiments, the size of the reference electrode 813 in the second extension direction may be in the range of 10 mm to 80 mm. In some embodiments, the size of the reference electrode 813 in the second extension direction may be in the range of 20 mm to 80 mm.
  • the size of the reference electrode 813 in the second extension direction may be in the range of 30 mm to 80 mm. It should be noted that the size of the reference electrode 813 along the second extension direction of the wearable structure 820 may also be smaller than the size of the first electrode 811 or the second electrode 812 along the second extension direction of the wearable structure 820, as long as it can provide a reference ground voltage.
  • FIG9 is a front structural schematic diagram of trousers according to some embodiments of the present specification.
  • the wearable device may be trousers 900.
  • the trousers 900 may include a first strain sensor 910, a first electrode 921, a second electrode 922, a reference electrode 923, and a second strain sensor 930.
  • the first strain sensor 910 is located on the wearable structure and fits closely to the waist area of the human body.
  • the first strain sensor 910 collects the breathing state information of the human body based on the fluctuation of the waist area when the human body breathes.
  • the first strain sensor 910 may be a belt-shaped/ring-shaped structure around the user's waist.
  • the first strain sensor 910 may be a belt-shaped sensor that is elastic and can be extended around the waist.
  • the circumferential circumference of the chest expands and the top of the diaphragm drops, causing the circumferential circumference of the first strain sensor 910 to change.
  • the first strain sensor 910 can measure the above-mentioned circumferential circumference change.
  • the circumferential circumference of the chest shrinks and the top of the diaphragm rises.
  • the first strain sensor 910 can measure the circumferential circumference change at this time.
  • the above-mentioned process of the change in the circumferential circumference of the inhalation movement and the exhalation movement is regarded as a breathing process to obtain the breathing state information of the human body.
  • the first strain sensor 910 may also be set not around the waist of the human body.
  • the first strain sensor 910 can be attached to the abdominal area of the human body. When the human body breathes, the abdominal area will fluctuate. The first strain sensor 910 is deformed based on the fluctuation of the abdominal area, thereby obtaining the user's breathing status information.
  • the number of the first strain sensors 910 may be one or more.
  • the multiple first strain sensors 910 may be fitted with different areas of the abdominal area, so as to obtain more accurate respiratory status information.
  • the first strain sensor 910 may include any one or more of a strain pressure sensor, a strain torque sensor, a strain displacement sensor, a strain acceleration sensor, etc.
  • the first strain sensor 910 may also be replaced by an air pressure sensor.
  • the air pressure sensor may include a closed flexible cavity and a pressure sensing element, and the air pressure sensor is sealed and connected to the flexible cavity. When the human body wears the air pressure sensor, the air pressure sensor fits the waist area or abdomen of the human body.
  • the ups and downs of the waist area or abdomen area can act on the flexible cavity of the air pressure sensor, and the internal pressure of the flexible cavity changes.
  • the pressure sensing element converts the pressure change into an electrical signal to obtain the changes in the waist area or abdomen area when the human body breathes, thereby determining the respiratory status information of the human body.
  • the number of air pressure sensors can be one or more. When the number of air pressure sensors is multiple, the multiple air pressure sensors can be distributed at different positions of the waist or abdomen area of the human body to obtain the ups and downs of different positions of the waist or abdomen area.
  • the respiratory state information can reflect the physiological structure changes of the human body during the breathing process.
  • the respiratory state information of the human body may include respiratory frequency, respiratory depth, inhalation time, exhalation time, and breathing stability.
  • the respiratory frequency, inhalation time, and exhalation time can be determined by measuring the time of the above-mentioned circumferential circumference changes.
  • the respiratory depth can be determined by measuring the peak value of the above-mentioned circumferential circumference changes (such as the circumference of the peak value).
  • the stability of breathing can be determined by measuring multiple breathing processes and comparing the breathing depth, inhalation time, and exhalation time between the multiple breathing processes. Real-time monitoring of the exhalation process can be achieved through the first strain sensor 910.
  • the first electrode 921 and the second electrode 922 can refer to the first electrode 311 and the second electrode 312 in Figures 3, 4, and 5, or refer to the first electrode 811 and the second electrode 812 in Figure 8.
  • at least two electrodes are spaced apart and distributed on the wearable structure.
  • at least two electrodes are located on both sides of the median sagittal plane of the human body, that is, the first electrode 921 can be located on the left side of the median sagittal plane of the human body, and the second electrode 922 can be located on the right side of the median sagittal plane of the human body.
  • the first electrode 921 can be located on the left leg side of the median sagittal plane of the human body, and the second electrode 922 can be located on the right leg side of the median sagittal plane of the human body.
  • at least two electrodes are located on both sides of the median sagittal plane of the human waist area.
  • the first electrode 921 can be located on the left side of the median sagittal plane of the waist region of the human body, and the second electrode 922 can be located on the right side of the median sagittal plane of the waist region of the human body.
  • the first electrode 921 and the second electrode 922 can be located on the inner side of the trousers 900, that is, close to the side of human skin.
  • the first electrode 921 and the second electrode 922 can be connected to the trousers 900 by Velcro, adhesive, buckle, mesh bag, etc.
  • the first electrode 921 and the second electrode 922 can obtain electrocardiographic signals.
  • the first electrode 921 and the second electrode 922 are respectively attached to the ilium, the back waist or the abdomen on both sides of the median sagittal plane of the human body.
  • the first electrode 921 and the second electrode 922 are attached to the back waist on both sides of the median sagittal plane of the human body, as shown in Figure 10.
  • the first electrode 921 and the second electrode 922 are symmetrically arranged about the midsagittal plane of the person.
  • the symmetrical arrangement of the electrodes can make the motion artifacts at the positions where the first electrode 921 and the second electrode 922 are attached have good consistency, which is conducive to eliminating motion artifacts.
  • the motion artifacts in the electrocardiogram signal can be eliminated by a differential amplifier circuit to improve the quality of the electrocardiogram signal.
  • the reference electrode 923 can provide a reference ground voltage.
  • the first electrode 921 and the second electrode 922 are spaced apart and arranged on the surface of the wearable structure pants 900 close to the human skin, and the reference electrode 923 is located between the first electrode 921 and the second electrode 922.
  • the reference electrode 923 can also be located on the inside of the pants 900.
  • the reference electrode 923 can refer to the reference electrode 813 in FIG. 8 .
  • the second strain sensor 930 can be used to obtain the motion parameters of the lower limbs of the human body.
  • the second strain sensor 930 can sense the bending and extension movements of the human joints.
  • Exemplary lower limb motion parameters may include joint bending angles, bending directions, etc.
  • the second strain sensor 930 may be located at a position corresponding to the leg or buttocks of the human body in the wearable structural pants 900.
  • FIG. 9 only depicts the situation of the second strain sensor 930 at the outside of the left knee joint.
  • the second strain sensor 930 can be located at any joint of the lower limbs of the human body, such as the right knee joint, ankle joint, hip joint, etc.; the second strain sensor 930 can be located at any position of any joint of the lower limbs of the human body, such as the outside of the left knee joint, the inside of the left knee joint, etc.
  • the second strain sensor 930 is located at the position corresponding to the buttocks of the human body in the wearable structural pants 900, as shown in FIG. 10.
  • the processor is further configured to identify the movement of the lower limbs of the human body based on the second strain sensor 930. For example, when the second strain sensor 930 obtains that the knee joint bending angle is 180° and the hip joint bending angle is 120°, the processor determines that the lower limb movement is squatting; when the knee joint bending angle is 0° and the hip joint bending angle is 0°, the processor determines that the lower limb movement is standing.
  • the second strain sensor 930 can realize real-time monitoring of the lower limb movement of the human body.
  • FIG. 10 is a schematic diagram of the back structure of pants according to some embodiments of the present specification.
  • the first strain sensor 910 can be fitted with the waist area of the human body.
  • the first electrode 921 and the second electrode 922 are fitted to the back waist on both sides of the median sagittal plane of the human body.
  • the first electrode 921 is fitted to the right back waist on both sides of the median sagittal plane of the human body
  • the second electrode 922 is fitted to the left back waist on both sides of the median sagittal plane of the human body.
  • the second strain sensor 930 is located at the position corresponding to the wearable structure trousers 900 and the human body's buttocks (right buttocks). It can be understood that the position of the above-mentioned components is only for illustration.
  • the above-mentioned components can be located at any position of the lower limbs of the human body.
  • the first electrode 921 and the second electrode 922 can also be set at the thigh, calf, knee joint, ankle and other positions.
  • Figure 11 is a standard electrocardiogram shown in some embodiments of the present specification.
  • the waveform 1101 shown in Figure 11 shows characteristics such as P wave, QRS complex, ST segment, T wave, P-R interval, U wave, etc.
  • the processor can determine the heart rate variability and heart rate of the human body based on the ECG signal.
  • the ECG signal acquired by at least two electrodes can be shown in Figure 11.
  • the heart rate can be expressed as the number of R waves that appear per unit time (e.g., one minute), for example, calculated by dividing 60 by the P-P interval or 60 by the R-R interval.
  • the processor can calculate one or more mathematical statistical indicators related to the RR interval through statistical analysis to determine the heart rate variability.
  • the heart rate variability can be presented as statistical indicators such as SDNN, SDANN, RMSSD, pNN50, SDNNi, etc.
  • the heart rate variability can be calculated by the following formula (1):
  • SDNN is the standard deviation of the interval between adjacent heartbeats; N is the number; RRs i is the RR interval; i is a different RR interval; SDNN can reflect It reflects the total tension of the sympathetic and parasympathetic nerves.
  • the normal value of SDNN is 100 milliseconds to 150 milliseconds, and the abnormal value of SDNN is less than 50 milliseconds.
  • heart rate variability can be calculated using the following formula (2) and formula (3):
  • SDANN is the average value of the RR interval every 5 minutes, and then the standard deviation of several intervals is calculated; RR s5 is the average value of the RR interval within 5 minutes; SDANN reflects the sympathetic nerve tension and is related to the slow-changing component of the heart rate. When the sympathetic nerve tension increases, its value decreases.
  • the normal value of SDANN is 80 milliseconds to 140 milliseconds, and the abnormal value of SDANN is less than 50 milliseconds.
  • the heart rate variability can be calculated by the following formula (4):
  • RMSSD is the root mean square value of the interval between adjacent heartbeats.
  • RMSSD can reflect the parasympathetic nerve tension and is related to the rapid change component of the heart rate. When the parasympathetic nerve tension decreases, its value decreases.
  • the normal value of RMSSD is 15 milliseconds to 45 milliseconds, and the abnormal value of RMSSD is less than 15 milliseconds.
  • the heart rate variability can be calculated by the following formula (5):
  • pNN50 is the proportion of adjacent heartbeat intervals with a difference of more than 50 milliseconds.
  • pNN50 reflects the parasympathetic nerve tension and is related to the rapid changes in heart rate. When the parasympathetic nerve tension decreases, its value decreases.
  • the normal value of pNN50 is 1% to 12%, and the abnormal value of pNN50 is less than 0.75%.
  • the heart rate variability can be calculated by the following formula (6) and formula (7):
  • SDNNi is the average value of the standard deviation of the heartbeat interval every 5 minutes in the 24-hour record; SDNNs 5 is the standard deviation of the heartbeat interval every 5 minutes.
  • SDNNi reflects the sympathetic nerve tension and is related to the slowly changing component of the heart rate. When the sympathetic nerve tension increases, its value decreases.
  • the normal value of SDNNi is 40 milliseconds to 80 milliseconds, and the abnormal value of SDNNi is less than 20 milliseconds. It can be understood that the mathematical and statistical indicators for determining heart rate variability can also be other mathematical and statistical indicators, and this specification does not limit this.
  • the processor may analyze the distribution of R-R intervals in the electrocardiogram signal to obtain the variability of the R-R intervals as the heart rate variability.
  • the variability of the R-R intervals may be obtained as the heart rate variability through an R-R interval histogram, an R-R interval difference histogram, a time series diagram of HRV, etc.
  • the processor may determine the heart rate variability by frequency domain analysis. For example, the overall heart rate variability is evaluated by summing the power of all frequency ranges of all normal heartbeat intervals; the power in the low frequency range represents the activity of the sympathetic and parasympathetic nerves; the power in the high frequency range represents the activity of the parasympathetic nerves, etc.
  • the processor may determine the heart rate variability by a nonlinear analysis method, for example, by analyzing fractal dimension (correlation dimension, Hausdorf dimension or information dimension), complexity analysis, Lyapaunov index, Kolmogorov entropy, approximate entropy analysis, etc.
  • the processor may determine the heart rate variability through an index of the electrocardiogram, for example, through a vector length index, a vector angle index, etc.
  • the processor may use a specific mathematical algorithm to establish a corresponding mathematical model to determine the heart rate variability, for example, by determining the heart rate variability through parameters such as fractal dimension, measure entropy, Lyapunov index, complexity, etc.
  • FIG. 12 is a schematic diagram of determining a human body's physical state based on heart rate variability according to some embodiments of the present specification.
  • the body state may include a relaxed state and a tense state.
  • the relaxed state may refer to the physiological state of the human body when it is awake, quiet, emotionally calm, and has a slight amount of exercise/no exercise.
  • the relaxed state may be manifested as a resting heart rate of 60 to 80 times/minute; the mathematical and statistical indicators related to the heart rate variability are normal values (greater than a preset threshold).
  • the tense state may refer to the physiological state of the human body when it is anxious, emotionally unstable, or performing quantitative exercise.
  • the tense state may be manifested as a heart rate of 80 to 100 times/minute; the mathematical and statistical indicators related to the heart rate variability are abnormal values (not greater than a preset threshold).
  • the processor may determine that the body state of the human body during exercise is a tense state when the heart rate variability is not greater than a preset threshold; when the heart rate variability is greater than a preset threshold, the body state of the human body during exercise is determined to be a relaxed state.
  • the preset threshold of heart rate variability can be determined by an empirical value. For example, when the mathematical statistical indicator of heart rate variability is SDNN, the preset threshold of heart rate variability can be 50 millisecond.
  • the processor can use a machine learning model that has completed training, at least with heart rate variability as input data, and the machine learning model outputs the physical state of the human body based on the input data.
  • Exemplary machine learning models may include deep neural network models, convolutional neural network models, etc.
  • the machine learning model can be obtained by training a large number of training samples with labels. Specifically, multiple groups of training samples with labels are input into the initial model, a loss function is constructed based on the output of the initial model and the label, and the parameters of the model are updated through training based on the loss function iteration.
  • training can be performed based on the training samples by various methods. For example, training can be performed based on the gradient descent method. When the preset conditions are met, the training ends and a trained model is obtained. Among them, the preset conditions can be the convergence of the loss function.
  • the training sample may include multiple historical heart rate variability data (such as SDNN, SDANN, RMSSD, pNN50, SDNNi and other statistical indicators).
  • the identifier may be the physical state of the human body corresponding to each historical heart rate variability data.
  • the machine learning model can realize intelligent recognition of the human body state, improve recognition efficiency, and reduce the error of the judgment result caused by subjective judgment of the physical state.
  • the processor can obtain a calibration curve and determine the physical state of the human body according to the calibration curve.
  • the calibration curve is obtained by fitting the heart rate variability generated by the human body in different physical states.
  • the calibration curve can be obtained by fitting the mathematical and statistical indicators of heart rate variability such as SDNN, SDANN, RMSSD, pNN50, SDNNi, etc.
  • the abscissa of the calibration curve can be time, and the ordinate can be the value of each mathematical and statistical indicator of heart rate variability.
  • the processor can use the curve segment that meets the normal value range in the calibration curve as the interval of the relaxed state, and the curve segment that does not meet the normal value range as the interval of the tense state.
  • the curve segment in its calibration curve that meets the SDNN value of 100 milliseconds to 150 milliseconds is used as the interval of the relaxed state, and the curve segment that meets the SDNN value of less than 50 milliseconds is used as the interval of the tense state.
  • FIG. 13 is a schematic diagram of determining a human body's physical state based on heart rate variability and heart rate according to some embodiments of the present specification.
  • Heart rate can also reflect the physical state of the human body.
  • the processor can determine the human body's physical state based on heart rate variability and heart rate.
  • the processor may determine that the body state of the human body during exercise is a tense state when the heart rate variability is not greater than a preset threshold and the heart rate is greater than a preset heart rate threshold; and determine that the body state of the human body during exercise is a relaxed state when the heart rate variability is greater than a preset threshold and the heart rate is not greater than a preset heart rate threshold.
  • the preset threshold of heart rate variability can be determined by an empirical value.
  • the preset threshold of heart rate variability can be 50 milliseconds.
  • the preset heart rate threshold of the heart rate can be set by an empirical value or the user's own physical health condition.
  • the preset heart rate threshold can be 60 times/minute, 70 times/minute, 80 times/minute, etc.
  • the processor can use a machine learning model that has completed training, at least with heart rate variability and heart rate as input data, and the machine learning model outputs the physical state of the human body based on the input data.
  • Exemplary machine learning models may include deep neural network models, convolutional neural network models, etc.
  • the machine learning model can be obtained by training a large number of training samples with labels. Specifically, multiple groups of training samples with labels are input into the initial model, a loss function is constructed based on the output of the initial model and the label, and the parameters of the model are updated through training based on the loss function iteration.
  • training can be performed based on training samples by various methods. For example, training can be performed based on the gradient descent method. When the preset conditions are met, the training ends and a trained model is obtained. Among them, the preset conditions can be the convergence of the loss function.
  • the training sample may include multiple historical heart rate variability data (such as SDNN, SDANN, RMSSD, pNN50, SDNNi and other statistical indicators) and multiple historical heart rate data.
  • the identifier may be the physical state of the human body corresponding to each historical heart rate variability data and the historical heart rate data.
  • the machine learning model can realize intelligent recognition of the human body state, improve recognition efficiency, and reduce the error of the judgment result caused by subjective judgment of the physical state; in addition, adding heart rate data to the model input increases the calculation circumference of the model and improves the degree of fit with the actual physiological condition.
  • FIG. 14 is a schematic diagram of determining a human body's physical state based on heart rate variability and respiratory state information according to some embodiments of this specification.
  • Breathing can also reflect the physical state of the human body.
  • the processor can determine the human body's physical state based on heart rate variability and breathing state information.
  • the processor can determine that the body state of the human body during exercise is a relaxed state when the heart rate variability is greater than a preset threshold and the respiratory state information is within the preset respiratory state information range; when the heart rate variability is not greater than the preset threshold and the respiratory state information is not within the preset respiratory state information range, the body state of the human body during exercise is determined to be a tense state.
  • the preset threshold of heart rate variability can be determined by an empirical value. For example, when the mathematical statistical indicator of heart rate variability is SDNN, the preset threshold of heart rate variability can be 50 milliseconds.
  • the respiratory state information may include respiratory frequency, respiratory depth, inhalation time, exhalation time, and the stability of breathing.
  • the respiratory state information can reflect the body state of the human body. For example, when the human body's respiratory frequency is normal, the respiratory depth is large, the inhalation time and exhalation time are long, and the breathing process is relatively stable, the human body can be judged to be in a relaxed state; when the human body's respiratory frequency is abnormal (such as too fast respiratory frequency), the respiratory depth is small, the inhalation time and exhalation time are short, and the breathing process is unstable, the human body can be judged to be in a tense state.
  • the preset respiratory state information range can be determined by empirical values. If the respiratory state information is respiratory frequency, the preset respiratory state information range can be 12 times/minute to 20 times/minute, etc. Further, the preset breathing state information range may also be a preset range corresponding to breathing depth, inhalation time, exhalation time, breathing stability, etc.
  • the processor may determine the body state of the human body based on at least one parameter in the heart rate variability and the respiratory state information.
  • the respiratory state information may include the respiratory frequency, and the processor may determine the body state of the human body based on the heart rate variability and the respiratory frequency.
  • determining the body state of the human body based on the heart rate variability and the respiratory frequency may include: when the body state heart rate variability of the human body is not greater than a preset threshold and the respiratory frequency is greater than the upper limit of the preset respiratory frequency range, the user's body state is a tense state; when the body state heart rate variability of the human body is not greater than the preset threshold and the respiratory frequency is within the preset respiratory frequency range, the user's body state is a relaxed state.
  • the respiratory state information may include the breathing depth, and the processor may determine the body state of the human body based on the heart rate variability and the breathing depth.
  • determining the body state of the human body based on the heart rate variability and the breathing depth may include: when the body state heart rate variability of the human body is not greater than the preset threshold and the respiratory frequency is less than the lower limit of the preset breathing depth range, the user's body state is a tense state; when the body state heart rate variability of the human body is not greater than the preset threshold and the breathing depth is within the preset breathing depth range, the user's body state is a relaxed state.
  • the respiratory state information may include the incoming call time, and the processor may determine the body state of the human body based on the heart rate variability and the incoming call time.
  • determining the body state of the human body based on the heart rate variability and the incoming call time may include: when the body state heart rate variability of the human body is not greater than the preset threshold and the incoming call time is less than the lower limit of the preset incoming call time range, the user's body state is a tense state; when the body state heart rate variability of the human body is not greater than the preset threshold and the incoming call time is within the preset incoming call time range, the user's body state is a relaxed state.
  • the respiratory state information may include the exhalation time
  • the processor may determine the body state of the human body based on the heart rate variability and the exhalation time.
  • determining the body state of the human body based on the heart rate variability and the exhalation time may include: when the body state heart rate variability of the human body is not greater than the preset threshold and the exhalation time is less than the lower limit of the preset exhalation time range, the user's body state is a tense state; when the body state heart rate variability of the human body is not greater than the preset threshold and the exhalation time is within the preset exhalation time range, the user's body state is a relaxed state.
  • the respiratory state information may include respiratory stability, wherein the respiratory stability may be calculated by the respiratory frequency, the respiratory depth, the inhalation time and the exhalation time, and the processor may determine the body state of the human body based on the heart rate variability and the stability of the breathing, etc. It should be noted that the processor can also determine the physical state of the human body based on multiple parameters in the heart rate variability and respiratory state information, which will not be repeated here.
  • the processor can use a machine learning model that has completed training, at least with heart rate variability and respiratory status information as input data, and the machine learning model outputs the physical state of the human body based on the input data.
  • Exemplary machine learning models may include deep neural network models, convolutional neural network models, etc.
  • the machine learning model can be obtained by training a large number of training samples with labels. Specifically, multiple groups of training samples with labels are input into the initial model, a loss function is constructed based on the output of the initial model and the label, and the parameters of the model are updated through training based on the loss function iteration.
  • training can be performed based on the training samples by various methods. For example, training can be performed based on the gradient descent method. When the preset conditions are met, the training ends and a trained model is obtained. Among them, the preset conditions can be the convergence of the loss function.
  • the training sample may include multiple historical heart rate variability data (such as statistical indicators such as SDNN, SDANN, RMSSD, pNN50, SDNNi, etc.) and multiple historical respiratory state information (such as any one or more of historical respiratory frequency, historical respiratory depth, historical inhalation time, historical exhalation time, historical breathing stability, etc.).
  • the identifier may be the physical state of the human body corresponding to each historical heart rate variability data and historical respiratory state information. Intelligent identification of the human body's physical state can be achieved through the machine learning model; in addition, adding respiratory state information to the model input increases the calculation circumference of the model and improves the degree of fit between the model and the actual physiological condition. It can be understood that parameters such as heart rate variability, heart rate, and respiratory state information can be used as inputs of the machine learning model at the same time to further improve the degree of fit between the model and the actual physiological condition.
  • different sports actions correspond to different heart rate variability
  • the processor is further configured to evaluate the sports action based on the heart rate variability and the sports action.
  • the sports action can be determined based on the second strain sensor 930 in Figures 9 and 10.
  • the processor, terminal device or database may pre-store motion parameters and heart rate variability data of at least one reference action. After the processor determines the current motion action based on the human lower limb motion parameters obtained by the second strain sensor 930, the processor may compare the motion parameters of the current motion action with the motion parameters of the reference action, and compare the heart rate variability of the current motion action with the heart rate variability of the reference action. When the similarity between the two is greater than the preset similarity threshold, the evaluation result is that the current motion action meets the standard of the reference action, which can also be understood as the current motion action is correct.
  • the processor can pre-store the motion parameters such as the joint bending angle, bending direction, etc. of yoga, Pilates, meditation, sitting meditation, standing, breathing training, etc., as well as the heart rate variability corresponding to the above motions. If the current joint bending angle, bending direction and the joint bending angle, bending direction of the reference motion are not less than the first threshold value (for example, 90%), and the current heart rate variability and the reference heart rate variability are not less than the second threshold value (for example, 90%), the evaluation result is that the current motion meets the standard of the reference motion.
  • the first threshold value for example, 90%
  • the current heart rate variability and the reference heart rate variability are not less than the second threshold value (for example, 90%)
  • the evaluation result is that the current motion does not meet the standard of the reference motion.
  • the processor can send feedback information to the user through the feedback module. For example, in response to the evaluation result being the current If the sports action does not meet the standards of the reference action, the processor can provide the user with specific action parameters through the feedback module and provide guidance plans, training plans, etc.
  • the processor is further configured to determine whether the movement action achieves a predetermined effect based on the heart rate variability and the movement action.
  • the predetermined effect means that the user's heart rate variability is not less than a preset threshold. Exemplarily, when the user's heart rate variability is not less than a preset threshold, the movement action achieves a predetermined effect.
  • the movement action fails to achieve the expected effect, and the processor can prompt the user to continue the current movement action or change the movement action through the feedback module until the user's heart rate variability is less than the preset threshold.
  • FIG15 is an exemplary structural diagram of another wearable device according to some embodiments of the present specification.
  • the wearable device 1500 may include at least two electrodes 210, a wearable structure 220, a processor 230, a feedback module 240, and an electromyographic module 250.
  • the at least two electrodes 210, the wearable structure 220, and the processor 230 may refer to the description of FIG2 .
  • the wearable device may further include a feedback module 240.
  • the feedback module is in communication with the processor, and the processor issues a control instruction in response to the physical state of the human body, and the feedback module issues feedback information to the user based on the control instruction.
  • the control instruction may be used to determine whether the feedback module 240 sends feedback information, and the specific content of the feedback information sent by the feedback module 240. Exemplary specific content of the feedback information may include training plans, action prompts, health reminders, etc.
  • the feedback module 240 may be configured as a speaker, and the speaker controls the speaker to enter a working state or switch an audio signal based on a control instruction.
  • the control instruction may be to determine that the feedback module 240 sends feedback information and switches the audio signal to music with a soothing tune; when the body state of the human body is in a relaxed state, the control instruction may not send feedback information.
  • the audio signal may be obtained through a network or by being pre-stored in a database.
  • the feedback module 240 may be configured as other devices, such as an electronic screen, a vibration massage device, etc.
  • the processor continues to judge the physical state of the human body based on at least the electrocardiogram signal. If the physical state of the human body is still in a tense state, the processor can switch the audio signal until the physical state of the human body enters a relaxed state.
  • the processor can monitor the physical state of the human body in real time through the electrode, the first strain sensor, and the second strain sensor, and determine the feedback information based on the result of the real-time monitoring. For example, when the feedback module 240 plays a soothing music, the physical state of the human body is still in a tense state, and the processor can switch the track until the physical state of the human body enters a relaxed state.
  • the feedback module 240 can also guide the user to switch actions to adjust the user from a tense state to a relaxed state. After the user's physical state is adjusted to a relaxed state, the feedback module 240 reminds the user to continue the previous target action. Specifically, when the processor determines that the user's physical state is in a tense state, the processor controls the feedback module 240 to send feedback information to the user, and the feedback information at least includes information that prompts the user to switch actions.
  • the processor determines that the user's physical state is in a tense state, and the processor controls the feedback module 240 to send a voice reminder to the user, reminding the user that the body is in a tense state, and recommends meditating or standing to adjust the body state.
  • the feedback module 240 can also guide the user to breathe to further improve the adjustment of the user's physical state.
  • the feedback module 240 guides the user to switch actions in a way that can include any one or more of voice reminders, text messages, video messages, vibrations, electric shocks, etc.
  • the body state of the human body is associated with the feedback information.
  • the feedback information may be information to relieve the tense state. For example, soothing music, comfortable audio, landscape pictures, guidance information (for example, including information for guiding breathing adjustment, information for guiding movement adjustment, etc.), etc.
  • the movement of the human body is associated with the feedback information.
  • the feedback information may include information such as guidance on yoga movements, training plans, and health reminders.
  • the wearable device may further include an electromyography module 250 .
  • the myoelectric module 250 can be used to collect myoelectric signals of the human body.
  • the processor can determine whether the human body's movement is standard based on one or more of the myoelectric signals, movement and breathing state information, heart rate or heart rate variability.
  • EMG signals can be obtained from many parts of the human body, such as the calf, thigh, buttocks, waist, back, chest, shoulder, arm, neck, etc.
  • the EMG signals obtained from different parts carry the movement and function information of the corresponding parts.
  • the EMG signals on the legs reflect the posture and movement state of the legs, such as walking, running, squatting, etc. Therefore, wearable devices can be used to collect EMG signals to meet people's requirements for sports and fitness guidance.
  • the human body when the human body is doing strength training, taking the dumbbell lateral raise as an example, when the human body's arms are stretched out to both sides, the muscles (especially the shoulder muscles) exert force, the EMG signals are significantly enhanced, and the human body is generally in the inhalation state; when the arms are closed, the EMG signals are weakened, and the user is generally in the exhalation state.
  • the strength of the EMG signals of the shoulder muscles of the human body under different breathing states can be judged to evaluate the various sub-movements in the human body's lateral raise action, so as to judge whether each sub-action is standard.
  • the processor, terminal device or database may pre-store one or more of the electromyographic signal, movement and breathing state information, heart rate or heart rate variability of at least one reference action.
  • the processor may compare the above parameters of the current movement action with the above parameters of the reference action. When the similarity is greater than a preset similarity threshold, the evaluation result is that the current movement action meets the standard of the reference action, that is, the user's movement action is correct; when the similarity of at least one parameter is less than the preset similarity threshold, the evaluation result is that the current movement action meets the standard of the reference action, that is, the user's movement action is correct; The previous motion does not meet the standard of the reference motion, and the user's current motion is wrong or non-standard.
  • the beneficial effects that may be brought about by the embodiments of this specification include but are not limited to: (1) Real-time monitoring of the human body's heart rate variability, heart rate, respiratory status information, and movement movements through various components of the wearable device to achieve real-time monitoring of the human body's tension/relaxation state; (2) Monitoring components such as electrodes and strain sensors are integrated on the wearable device, and the impact of the above monitoring components on wearing comfort is reduced through the design of structure and fabric; (3) Parameters such as heart rate variability, heart rate, and respiratory status information are used as model inputs through machine learning models to increase the input circumference of the model and improve the degree of fit between the model and the actual physiological conditions; (4) Based on the tension/relaxation state monitoring results and the judgment results of the movement movements, different content feedback is provided to improve the user's experience in different usage scenarios; (5) The wearable device can be applied to various application scenarios such as physical exercise, psychological testing, clinical health monitoring, movement correction, rehabilitation treatment, etc. It should be noted that different embodiments may produce different beneficial effects. In different
  • the present application uses specific words to describe the embodiments of the present application.
  • “one embodiment”, “an embodiment”, and/or “some embodiments” refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that “one embodiment” or “an embodiment” or “an alternative embodiment” mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment.
  • some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Pathology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Cardiology (AREA)
  • Physiology (AREA)
  • Pulmonology (AREA)
  • Signal Processing (AREA)
  • Psychiatry (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Dermatology (AREA)
  • Developmental Disabilities (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Child & Adolescent Psychology (AREA)
  • Evolutionary Computation (AREA)
  • Educational Technology (AREA)
  • Hospice & Palliative Care (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

本说明书提供一种可穿戴设备，包括：至少两个电极，被配置为贴合人体皮肤以采集所述人体的心电信号；穿戴结构，被配置为承载所述至少两个电极，并将所述至少两个电极贴合在所述人体的正中矢状面的两侧；以及处理器，基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态。

Description

一种可穿戴设备
交叉引用
本申请要求于2022年11月17日提交的申请号为PCT/CN2022/132671的PCT申请的优先权，其全部内容通过引用并入本文。
技术领域
本申请涉及可穿戴设备领域，特别涉及一种能够确定身体状态的可穿戴设备。
背景技术
目前用于人体的身体状态检测的可穿戴设备中，对体温、心率、肌电等参数有较为深入的研究。然而，对于热衷于静态运动(例如，瑜伽、普拉提、冥想、打坐、呼吸训练等运动)的用户来说，需要将自身处于相对放松的身体状态，才能达到相应的修养身心的效果。但是目前的可穿戴设备并不能判断用户锻炼时处于怎样的身体状态，也无法引导用户在锻炼中对身体状态进行调节。
因此，有必要提供一种能够确定人体的身体状态以及调整身体状态的可穿戴设备。
发明内容
本说明书实施例之一提供一种可穿戴设备，包括：至少两个电极，被配置为贴合人体皮肤以采集所述人体的心电信号；穿戴结构，被配置为承载所述至少两个电极，并将所述至少两个电极贴合在所述人体的正中矢状面的两侧；以及处理器，基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态。
附图说明
本申请将以示例性实施例的方式进一步说明，这些示例性实施例将通过附图进行详细描述。这些实施例并非限制性的，在这些实施例中，相同的编号表示相同的结构，其中：
图1是根据本说明书一些实施例所示的可穿戴设备的示例性应用场景图；
图2是根据本说明书一些实施例所示的可穿戴设备的示例性结构图；
图3是根据本说明书一些实施例所示的可穿戴设备的平面示意图；
图4是根据本说明书一些实施例所示的可穿戴设备的结构示意图；
图5是根据本说明书一些实施例所示的可穿戴设备的结构示意图；
图6是人体全身示意图；
图7是人体腰部区域的髂骨示意图；
图8是根据本说明书一些实施例所示的可穿戴设备的结构示意图；
图9是根据本说明书一些实施例所示的裤装的正面结构示意图；
图10是根据本说明书一些实施例所示的裤装的背面结构示意图；
图11是根据本说明书一些实施例所示的一个标准的心电图；
图12是根据本说明书一些实施例所示的根据心率变异性确定人体的身体状态的示意图；
图13是根据本说明书一些实施例所示的根据心率变异性以及心率确定人体的身体状态的示意图；
图14是根据本说明书一些实施例所示的根据心率变异性以及呼吸状态信息确定人体的身体状态的示意图；以及
图15是根据本说明书一些实施例所示的另一可穿戴设备的示例性结构图。
具体实施方式
为了更清楚地说明本申请实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单的介绍。显而易见地，下面描述中的附图仅仅是本申请的一些示例或实施例，对于本领域的普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图将本申请应用于其它类似情景。除非从语言环境中显而易见或另做说明，图中相同标号代表相同结构或操作。
本说明书的实施例提供一种可穿戴设备，包括：至少两个电极，被配置为贴合人体皮肤以采集人体的心电信号；穿戴结构，被配置为承载至少两个电极，并将至少两个电极贴合在人体的正中矢状面的两侧腰部区域；以及处理器，基于心电信号确定人体的心率变异性，并至少根据心率变异性确定人体的身体 状态。本说明书提供的可穿戴设备可以通过实时检测人体的心率变异性(HeartRateVariability，HRV)以及心率、呼吸状态信息等参数，确定人体的身体状态，并将身体状态以及参考性的健康建议向用户实时反馈，实现用户健康监控过程的闭环；另外，用户的身体状态可以包括紧张状态、放松状态等情况，确定身体状态可以应用于体育锻炼、心理测试、临床健康监控等多方面的领域。
图1是根据本说明书一些实施例所示的可穿戴设备的示例性应用场景图。
如图1所示，应用场景100可以包括处理设备110、网络120、可穿戴设备130、终端设备140以及反馈模块150。在一些实施例中，应用场景100可以应用于体育锻炼、心理测试、临床健康监控等多种场景。
在一些实施例中，应用场景100可以用于对用户运动时的身体状态进行测量。示例性的运动可以包括瑜伽、普拉提、冥想、打坐、站桩、呼吸训练等。示例性的身体状态可以包括放松状态以及紧张状态。
在一些实施例中，处理设备110可以包括处理器。在一些实施例中，处理设备110可以用于处理可穿戴设备130、终端设备140产生的电信号。例如，处理设备110可以通过网络120接收穿戴设备130传输的心电信号；处理设备110可以通过网络120接收用户通过终端设备140输入的输入信息。在一些实施例中，处理设备110可以基于心电信号确定人体的心率变异性，并至少根据心率变异性确定人体的身体状态。在一些实施例中，处理设备110可以基于心电信号确定人体的心率变异性和心率，根据心率变异性和心率确定人体的身体状态。在一些实施例中，处理设备110可以通过完成训练的机器学习模型，至少以心率变异性作为输入数据，机器学习模型基于输入数据输出人体的身体状态。在一些实施例中，处理设备110可以确定标定曲线，并根据标定曲线确定人体的身体状态。在一些实施例中，处理设备110可以根据心率变异性和呼吸状态信息确定人体的身体状态。在一些实施例中，处理设备110可以通过完成训练的机器学习模型，至少以心率变异性和呼吸状态信息作为输入数据，机器学习模型基于输入数据输出人体的身体状态。在一些实施例中，处理设备110可以基于第二应变传感器识别人体相应部位的运动动作，这里相应部位的运动动作与第二应变传感器所处的位置相关。例如，当第二应变传感器位于人体腿部或臀部时，处理设备110可以基于第二应变传感器识别人体下肢的运动动作。在一些实施例中，处理设备110可以响应于人体的身体状态发出控制指令。
在一些实施例中，处理设备110可以是本地的或者远程的。例如，处理设备110可以直接或通过网络120访问存储于可穿戴设备130和/或终端设备140中的信息。在一些实施例中，处理设备110可以直接与可穿戴设备130和/或终端设备140连接以访问存储于其中的信息。例如，处理设备110可以位于可穿戴设备130中，并通过网络120实现与终端设备140的信息交互。又例如，处理设备110可以位于终端设备140中，并通过网络120实现与可穿戴设备130的信息交互。在一些实施例中，处理设备110可以在云平台上执行。例如，该云平台可以包括私有云、公共云、混合云、社区云、分散式云、内部云等中的一种或其任意组合。
在一些实施例中，处理设备110可以包含一个或多个子处理设备(例如，单芯处理设备或多核多芯处理设备)。仅仅作为示例，处理设备110可包含中央处理器(CPU)、专用集成电路(ASIC)、专用指令处理器(ASIP)、图形处理器(GPU)、物理处理器(PPU)、数字信号处理器(DSP)、现场可编程门阵列(FPGA)、可编辑逻辑电路(PLD)、控制器、微控制器单元、精简指令集电脑(RISC)、微处理器等或以上任意组合。
网络120可以促进应用场景100中各组件之间的数据和/或信息的交换。在一些实施例中，应用场景100中的一个或多个组件(例如，处理设备110、可穿戴设备130、终端设备140、反馈模块150)可以通过网络120发送数据和/或信息给应用场景100中的其他组件。例如，终端设备140产生的电信号可以通过网络120传输至处理设备110。在一些实施例中，网络120可以是任意类型的有线或无线网络。例如，网络120可以包括缆线网络、有线网络、光纤网络、电信网络、内部网络、网际网络、区域网络(LAN)、广域网络(WAN)、无线区域网络(WLAN)、都会区域网络(MAN)、公共电话交换网络(PSTN)、蓝牙网络、ZigBee网络、近场通讯(NFC)网络等或以上任意组合。在一些实施例中，网络120可以包括一个或多个网络进出点。例如，网络120可以包含有线或无线网络进出点，如基站和/或网际网络交换点120-1、120-2、…，通过这些进出点，应用场景100的一个或多个组件可以连接到网络120上以交换数据和/或信息。
可穿戴设备130是指具有穿戴功能的服装或设备。在一些实施例中，可穿戴设备130可以包括但不限于裤装、腰带等。可穿戴设备130可以用于获取用户的心电信号、肌电信号，并将上述信号通过网络120传输至处理设备110。在一些实施例中，可穿戴设备130可以设置至少两个电极，被配置为贴合人体皮肤以采集人体的心电信号。在一些实例中，可穿戴设备130还包括第一应变传感器，第一应变传感器基于人体呼吸时腰部区域的起伏变化采集人体的呼吸状态信息。在一些实施例中，可穿戴设备130还包括第二应变传感器，第二应变传感器可以位于用户的腿部、臀部、手臂、肩部、手部等位置。仅作为示例性说 明，第二应变传感器可以位于用户的腿部或臀部，此时第二应变传感器可以用于获取人体下肢动作参数。示例性的下肢动作参数可以包括关节弯曲角度、弯曲方向等。又例如，第二应变传感器可以位于用户的手臂或肩部，此时第二应变传感器可以用于获取手臂的动作参数(比如，手臂的弯曲角度、弯曲方向等)。在一些实施例中，可穿戴设备130还可以包括肌电模块，肌电模块用于采集人体的肌电信号。
需要注意的是，可穿戴设备130并不限于图1中所示的裤装，还可以包括其他设备，例如，腰带、裙装、护腕、护肘、护肩、护膝、袜子等，在此不做限定，任何可以使用本说明书的设备都在本说明书的保护范围内。
终端设备140可以是与用户进行交互的设备。在一些实施例中，终端设备140可以集成于可穿戴设备130上。终端设备140可以接收用户输入的输入信息，并将输入信息通过网络120发送至处理设备110。在一些实施例中，终端设备140可以通过网络120接收处理设备110发送的反馈信息，并将反馈信息呈现给用户。示例性的反馈信息可以包括当前身体状态的信息、运动建议、健康提醒等信息。在一些实施例中，终端设备140可以是移动智能终端141、平板电脑142、笔记本电脑143等。在一些实施例中，终端设备140可以集成在处理设备110、可穿戴设备130上。在一些实施例中，终端设备140可以是其他设备。例如，手机、智能家居装置、智能行动装置、虚拟现实装置、增强现实装置等，或其任意组合。在一些实施例中，智能家居装置可以包括智能电器的控制装置、智能监测装置、智能电视、智能摄像机等，或其任意组合。在一些实施例中，智能行动装置可以包括智能电话、个人数字助理(PDA)、游戏装置、导航装置、POS装置等，或其任意组合。在一些实施例中，虚拟现实装置和/或增强现实装置可以包括虚拟现实头盔、虚拟现实眼镜、虚拟现实眼罩、增强现实头盔、增强现实眼镜、增强现实眼罩等，或其任意组合。
反馈模块150可以用于基于控制指令向用户发出反馈信息。在一些实施例中，反馈模块150可以是扬声器、电子屏幕、震动感应装置等设备。在一些实施例中，反馈模块150可以是终端设备140的一部分，或集成于终端设备140中。在一些实施例中，反馈模块150也可以集成在可穿戴设备130上，并相对于终端设备140独立设置。
在一些实施例中，应用场景100还可以包括其他设备或组件，例如，数据库。
数据库可以存储数据，例如，终端设备140接收的生理参数信息(如心电信号、肌电信号、心率、心率变异性、呼吸状态信息等)。在一些实施例中，数据库可以存储从可穿戴设备130和/或移动终端设备获取的信息。在一些实施例中，数据库可以包括大容量存储器、可移动存储器、挥发性读写存储器(例如，随机存取存储器RAM)、只读存储器(ROM)等，或其任意组合。在一些实施例中，数据库可以与网络120连接以与应用场景100的一个或多个组件(例如，处理设备110、可穿戴设备130、终端设备140、移动终端设备等)通讯。应用场景100的一个或多个组件可以通过网络120访问存储于数据库中的数据。在一些实施例中，数据库可以是处理设备110的一部分。
图2是根据本说明书一些实施例所示的可穿戴设备的示例性结构图。
如图2所示，可穿戴设备200可以包括至少两个电极210、穿戴结构220以及处理器230。至少两个电极210可以采集人体的心电信号，处理器可230可以将心电信号处理为心率、心率变异性等信息，通过上述信息确定人体的身体状态。
至少两个电极210可以被配置为贴合人体皮肤以采集人体的心电信号。在一些实施例中，电极210的数量可以是2个、3个或其他数量。在一些实施例中，至少两个电极210可以位于人体的正中矢状面两侧位置。例如，至少两个电极210可以位于人体的正中矢状面两侧的髂骨、后腰或腹部或者双腿等区域贴合，以采集人体的心电信号。在一些实施例中，电极210可以通过魔术贴、网袋、卡扣、热压贴合、胶粘、缝合等方式固定在穿戴结构220上。在一些实施例中，电极210可以包括第一电极、第二电极，第一电极和第二电极可以分别位于人体的正中矢状面两侧位置。关于电极210的具体说明，参见图3及其相关描述。
穿戴结构220可以被配置为承载至少两个电极210，并将至少两个电极210贴合在人体的正中矢状面的两侧区域。穿戴结构220可以是穿戴舒适透气的面料、如棉、麻、尼龙等面料。在一些实施例中，穿戴结构220可以是裤装(如短裤、长裤、连衣裤)、腰带、裙装、护膝、护腰的一个或组合。关于穿戴结构220的具体说明，参见图3、图4、图5及其相关描述。
处理器230可以被配置为基于心电信号确定人体的心率变异性，并至少根据心率变异性确定人体的身体状态。处理器230可以集成在穿戴结构220上的任意位置，或为相对于穿戴结构220独立设置，例如，处理器230可以设置于云服务器中。在一些实施例中，处理器230可以基于心电信号确定人体的心率变异性，并至少根据心率变异性确定人体的身体状态。在一些实施例中，处理器230可以基于心电信号确定人体的心率变异性和心率，根据心率变异性和心率确定人体的身体状态。在一些实施例中，处理器230可以通过完成训练的机器学习模型，至少以心率变异性作为输入数据，机器学习模型基于输入数据输出人体的身体状态。在一些实施例中，处理器230可以确定标定曲线，并根据标定曲线确定人体的身体状态。在一些实施例中，处理器230可以根据心率变异性和呼吸状态信息确定人体的身体状态。在一些实施例中， 处理器230可以通过完成训练的机器学习模型，至少以心率变异性和呼吸状态信息作为输入数据，机器学习模型基于输入数据输出人体的身体状态。在一些实施例中，处理器230可以基于第二应变传感器识别人体下肢的运动动作。在一些实施例中，处理器230可以响应于人体的身体状态发出控制指令。关于处理器230的具体说明，参见图11至图16及其相关描述。
心率变异性可以体现心脏每次跳动时间间隔的变化情况。健康的心脏具有不规律性，而心脏的不规律性是由自主神经控制的，心率变异性可以反映神经系统的健康。自主神经分为两种，一种为控制“战斗或逃跑”的交感神经，另一种为控制“放松或消化”的副交感神经。处于交感神经控制情况下，心率变异性会降低，而处于副交感神经控制下，心率变异性会提高。人体处于放松情况下，心脏每次跳动的时间间隔差异大。而高度紧张时心脏每次跳动的时间间隔差异小，甚至实现等时间间隔的规律跳动。
心率变异性的分析方法可以包括线性分析法和非线性分析法。其中，线性分析法可以包括时域分析和频域分析。时域分析进一步包括统计学分析和几何图形分析等。非线性分析方法可以包括图像法和非线性参数计算法。图像法可以包括心电散点图法。非线性分析法可以包括分维数(相关维、Hausdorf维或信息维)分析法、复杂度分析法、Lyapaunov指数、哥式(Kolmogorov)熵。近似熵分析等。心率变异性的研究可以更好地反映可穿戴设备与人体交互过程中一些生理数据的变化。
图3是根据本说明书一些实施例所示的可穿戴设备的平面示意图。图4和图5是根据本说明书一些实施例所示的可穿戴设备的结构示意图。
结合图3～图5所示，可穿戴设备300可以包括第一电极311、第二电极312和穿戴结构320。
第一电极311和第二电极312用于人体皮肤贴合并采集人体的心电信号。在一些实施例中，第一电极311和第二电极312可以贴合于人体的不同部位，其中，第一电极311所贴合的部位可以具有第一电位，第二电极312所贴合的部位具有第二电位，第一电位和第二电位之间的差值可以用于反映人体的心电信号。
穿戴结构320用于承载第一电极311和第二电极312，并将第一电极311和第二电极312贴合在人体的正中矢状面的两侧。其中，第一电极311和第二电极312可以间隔分布于穿戴结构320上，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别位于人体的正中矢状面的两侧，分别检测人体的正中矢状面两侧的电位(例如，第一电位和第二电位)，根据人体的正中矢状面两侧的电位之间的差值来确定心电信号。这里将穿戴结构320佩戴于人体腰部区域，使第一电极311和第二电极312分别位于人体的正中矢状面的两侧，也就是说，第一电极311和第二电极312分别位于身体的两侧，而人体的正中矢状面两侧的电位之间的差值可以反映心电信号的强弱，通过增大第一电极311和第二电极312之间的间距，可以使得心电信号的强度变大。在一些实施例中，如图3所示，穿戴结构320可以具有第一延伸方向和与第一延伸方向垂直的第二延伸方向，第一电极311和第二电极312可以沿穿戴结构的第一延伸方向间隔分布。在一些实施例中，穿戴结构320的第一延伸方向可以是指人体在佩戴穿戴结构320时，穿戴结构320佩戴在人体腰部区域的周向方向。
在一些实施例中，穿戴结构320可以为具有弹性的带状结构，第一电极311和第二电极312可以沿带状结构的延伸方向(或称为长度方向)间隔分布在带状结构上，用户可以通过将带状结构系在或通过卡扣件固定在在自身的腰部区域，使得第一电极311和第二电极312可以分别位于人体的正中矢状面的两侧。其中，穿戴结构320的第一延伸方向为带状结构的延伸方向。在一些实施例中，穿戴结构320可以是图4中示出的腰带的形式，第一电极311和第二电极312可以间隔分布设置在腰带与人体皮肤贴合的表面上。其中，穿戴结构320的第一延伸方向可以是腰带展开后的长度方向。在一些实施例中，腰带的两端可以使用魔术贴或卡扣等方式结合，方便用户佩戴或脱下。穿戴结构320为具有弹性的带状结构，可以保证用户具有较好的佩戴舒适度，并且耐洗涤。在一些实施例中，穿戴结构320还可以是图5中示出的裤装(例如，短裤、长裤、连衣裤等)的形式，第一电极311和第二电极312可以沿裤装的裤腰的周向间隔分布设置在裤腰内侧，当人体穿戴裤装时，裤腰处的第一电极311和第二电极312可以分别位于人体的正中矢状面的两侧。例如，第一电极311和第二电极312可以分别位于左腰侧和右腰侧、左臀部和右臀部、左膝关节和右膝关节、左脚踝和右脚踝等位置(如图5中虚线矩形位置所示)。在一些实施例中，穿戴结构320还可以以裙装的形式，第一电极311和第二电极312可以沿裙装的裙腰的周向间隔分布设置在裙腰内侧，使得第一电极311和第二电极312可以分别位于人体的正中矢状面的两侧。其中，穿戴结构320的第一延伸方向可以是裤腰或裙腰沿其周向展开后的长度方向。
在一些实施例中，可穿戴设备300中的第一电极311和第二电极312还可以与穿戴结构320可拆卸连接。例如，穿戴结构320为带状结构或裤装，第一电极311和第二电极312中不与人体皮肤接触的一侧与带状结构或裤装可以通过粘接、卡接、嵌接等方式与穿戴结构320连接。当用户需要测量心电信号时，将第一电极311和第二电极312安装在带状结构或裤装的裤腰、裤腿末端、裤腿中部、臀部位置上，同时也可以根据用户自身的体型(例如，身高、体重、腰围等)调整第一电极311和第二电极312在穿戴结构 320上的位置，以适用于不同体型的用户。当用户需要对穿戴结构320(例如，带状结构或裤装)进行清洗时，可以将第一电极311和第二电极312等元件从穿戴结构320上卸下，以防止可穿戴设备300在清洗过程中对第一电极311和第二电极312等其它元件造成损伤。在一些实施例中，穿戴结构320(例如，带状结构或裤装的裤腰)与用户贴合的一侧可以设置第一魔术贴，第一魔术贴可以沿带状结构的延伸方向或裤腰的周侧分布，第一电极311、第二电极312与用户身体接触侧相背离的一侧设有第二魔术贴，第二魔术贴可以与第一魔术贴粘接，以实现第一电极311、第二电极312与可穿戴结构320可拆卸连接。在一些实施例中，第一魔术贴可以覆盖穿戴结构320上与用户相贴合的一侧。第一魔术贴为一个整体结构时，会影响穿戴结构320的自身弹性，导致用户穿戴体验感不佳，在一些实施例中，第一魔术贴的数量可以为多个，多个第一魔术贴依次拼接在穿戴结构320上与用户相贴合的一侧，此时，多个第一魔术贴之间存在缝隙可以使得穿戴结构320保持自身的弹性，同时也便于调整第一电极311和第二电极312在穿戴结构320的位置，从而适用于不同体型的用户。在一些实施例中，多个第一魔术贴也可以在沿带状结构的延伸方向或电极位置的周侧分布间隔。例如，每个第一魔术贴的长度可以为4cm，相邻的两个第一魔术贴之间的间距为1cm。在一些实施例中，第一魔术贴或第二魔术贴可以为采用无纺布材质制成，其材质较为柔软，用户在佩戴可穿戴设备300时，用户腰部的皮肤与第一魔术贴接触时，第一魔术贴不会给用户带来不适感，从而提高用户佩戴可穿戴设备300的用户体验感。在一些实施例中，可穿戴设备300中可以包括多组电极模块(例如，第一电极311和第二电极312)，每组电极模块上均设有与导线，该导线的一端与电极模块中的第一电极311或第二电极312连接，导线的另一端设置有接口插件，接口插件可以可穿戴设备300的电路结构电连接，例如电路结构的电路板上设置有与接口相适配的端口。用户可以根据自身情况，更换不同的电极模块。在一些实施例中，多个电极模块的尺寸可以相同或不同。仅作为示例性说明，电极模块中第一电极或第二电极尺寸可以为1cm*1cm，1cm*2cm，1cm*3cm，1cm*4cm，1cm*5cm、2cm*2cm，2cm*3cm，2cm*4cm，2cm*5cm、3cm*3cm，3cm*4cm，3cm*5cm、4cm*4cm，4cm*5cm或5cm*5cm。需要注意的是，电极模块中的第一电极或第二电极不限于上述列举的尺寸，还也可以为其它尺寸。
在一些实施例中，第一电极311或第二电极312可以以柔性贴片的形式人体皮肤贴合，该贴片可以为圆形、椭圆形、矩形、菱形等规则形状或其他不规则形状。在实际应用中，可以根据第一电极311或第二电极312贴合在腰部区域皮肤下的骨骼形状来设计第一电极311或第二电极312的形状。在一些实施例中，第一电极311或第二电极312可以是由单一材料制成的电极，例如金属织物电极、导电硅电极、水凝胶电极、金属电极等。优选地，第一电极311或第二电极312可以为金属织物电极和导电硅电极。进一步优选地，第一电极311或第二电极312可以为金属织物电极，金属织物电极电阻率更小，其阻抗以及与皮肤之间的接触阻抗也较小。第一电极311或第二电极312与皮肤之间的接触阻抗越小，有利于降低运动伪迹对第一电极311和第二电极312所采集的心电信号的干扰。在一些实施例中，第一电极311或第二电极312可以凸出于其周围的穿戴结构320的表面，这样可以为第一电极311或第二电极312提供的预压力，有利于第一电极311或第二电极312与人体皮肤充分贴合，以准确采集心电信号。在一些实施例中，第一电极311或第二电极312相对于其周围的穿戴结构320的表面凸出的高度取决于第一电极311或第二电极312的厚度。其中，第一电极311或第二电极312的厚度可以是指第一电极311或第二电极312在垂直于其周围的穿戴结构320的表面的方向上的尺寸。第一电极311或第二电极312的厚度与其材质相关。例如，在一些实施例中，在使用金属织物电极采集心电信号时，金属织物电极的厚度可以为10μm～5mm。优选地，金属织物电极的厚度可以为100μm～3mm。进一步优选地，金属织物电极的厚度可以为500μm～2mm。在一些实施例中，第一电极311或第二电极312还可以是不同材料叠加形成的电极，例如金属织物材料与导电硅材料构成的电极，不仅其与皮肤之间的接触阻抗小，并且其中与皮肤接触的导电硅具有亲肤、耐洗涤、耐摩擦等优点，避免电极与皮肤接触给人体带来的不适感。
在一些实施例中，第一电极311或第二电极312在第一延伸方向的尺寸或在第二延伸方向上的尺寸在不超过穿戴结构320在第一延伸方向的尺寸或在第二延伸方向的尺寸的情况下，第一电极311或第二电极312在第一延伸方向的尺寸或在第二延伸方向的尺寸应尽可能大，这样可以保证第一电极311或第二电极312与皮肤具有较大的贴合面积，以减小第一电极311或第二电极312与所贴合的皮肤之间的接触阻抗，从而可以降低运动伪迹对心电信号的干扰。同时，第一电极311或第二电极312在第一延伸方向的尺寸或在第二延伸方向的尺寸较大可以使得第一电极311或第二电极312在穿戴过程中更不易受到变形和偏移的影响，这样有利于降低运动伪迹，提高心电信号质量。除此之外，第一电极311或第二电极312在第一延伸方向的尺寸或在第二延伸方向的尺寸较大可以保证在各种运动或褶皱的情况下，第一电极311或第二电极312不至于完全从皮肤上脱落，而影响心电信号的采集。在一些实施例中，第一电极311或第二电极312在第一延伸方向的尺寸或在第二延伸方向上的尺寸可以分别是指第一电极311或第二电极312在第一延伸方向的最大尺寸或在第二延伸方向的最大尺寸。在一些实施例中，第一电极311或第二电极312在第一延伸方向的尺寸可以在5mm～50mm的范围内，第一电极311或第二电极312在第二延伸方向的尺寸 可以在5mm～50mm的范围内。在一些实施例中，第一电极311或第二电极312在第一延伸方向的尺寸可以在10mm～45mm的范围内，第一电极311或第二电极312在第二延伸方向的尺寸可以在10mm～45mm的范围内。在一些实施例中，第一电极311或第二电极312在第一延伸方向的尺寸可以在15mm～40mm的范围内，第一电极311或第二电极312在第二延伸方向的尺寸可以在15mm～40mm的范围内。在一些实施例中，第一电极311或第二电极312在第一延伸方向的尺寸可以在20mm～30mm的范围内，第一电极311或第二电极312在第二延伸方向的尺寸可以在20mm～30mm的范围内。
在一些实施例中，第一电极311或第二电极312与穿戴结构320靠近人体皮肤的表面之间可以设置有隔离层，具体地，隔离层位于第一电极311或第二电极312周围的穿戴结构320的表面，隔离层可以避免穿戴结构320在被浸湿的情况下将第一电极311或第二电极312意外接通，产生采集到的心电信号弱、失准的情况。在一些实施例中，隔离层可以由绝缘且防水的材料制成。在一些实施例中，隔离层的材料可以包括橡胶、高分子聚合物、硅胶等，或其任意组合。
本说明书实施例提供的可穿戴设备300可被佩戴在人体的腰部区域、臀部区域、膝关节区域、脚踝处区域等，使得第一电极311和第二电极312分别位于人体正中矢状面的两侧，并且与人体的正中矢状面两侧的腰部区域的皮肤贴合，以采集对人体的心电信号，这样既能实现对佩戴可穿戴设备300的用户的心脏状况监控，也能保证用户具有较好的佩戴舒适度。在一些实施例中，第一电极311和第二电极312在人体的正中矢状面两侧的腰部区域的贴合位置对采集到的心电信号的质量、强度相关。下面将结合具体的人体示意图对第一电极311和第二电极312与人体皮肤的贴合位置进行详细描述。
图6是人体全身示意图。其中，图6中的(a)示出了人体的正面，图6中的(b)示出了人体的背面。图7是人体腰部区域的髂骨示意图。其中，图7中的(a)示出了髂骨的正面，图7中的图(b)示出了髂骨的背面。
如图6中图(a)和图(b)所示，人体的正中矢状面是指通过人体的正中线601且将人体分为相等或近似的两部分的平面。其中，人体的正中线601可以根据从人体的鼻尖到两乳头中间的连线、从两乳头中间到腹部脐中间的连线或从腹部脐的中间到耻骨联合关节中间的连线来确定。人体的腰部区域可以是人体肋骨下端到髂骨下端之间的区域，该区域主要包括腹部和髂骨部分。在一些实施例中，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别贴合在人体的正中矢状面两侧的腰部区域的皮肤上。人体的正中矢状面可以将人体的腰部区域分为左腰区域和右腰区域，在一些实施例中，第一电极311和第二电极312可以分别贴合于左腰区域和右腰区域的皮肤上。在一些实施例中，第一电极311和第二电极312可以分别贴合于左臀部和右臀部的皮肤上。在一些实施例中，第一电极311和第二电极312可以分别贴合于左膝关节和右膝关节的皮肤上。在一些实施例中，第一电极311和第二电极312可以分别贴合于左脚踝和右脚踝的皮肤上。可以理解的是，第一电极311和第二电极312还可以分别贴合于其他部位，如贴合于左大腿和右大腿的皮肤上、左小腿和右小腿的皮肤上等。在一些实施例中，第一电极311和第二电极312可以关于人体的正中矢状面对称设置，这样可以使得第一电极311和第二电极312所贴合的位置的运动伪迹具有较好的一致性，有利于运动伪迹的消除，例如，可以通过差分放大电路来消除心电信号中的运动伪迹，以提高心电信号的质量。在一些实施例中，通过保持第一电极311和第二电极312的一致性，可以进一步提高第一电极311和第二电极312所贴合的位置的运动伪迹之间的一致性，更有利于运动伪迹的消除，使心电信号具有更高的质量。在一些实施例中，第一电极311和第二电极312一致可以包括材料、尺寸(例如，在第一延伸方向的尺寸、在第二延伸方向的尺寸以及厚度等)、对皮肤的压力等或其组合一致。在一些实施例中，还可以通过穿戴结构320承载第一电极311和第二电极312的部分弹性一致，来使得第一电极311和第二电极312所贴合的位置的运动伪迹具有较好的一致性，以便于消除运动伪迹，提高心电信号的质量。需要说明的是，在一些实施例中，第一电极311和第二电极312也可以关于人体的正中矢状面非对称设置，例如，当用户佩戴可穿戴设备时，第一电极311位于正中矢状面一侧的人体腹部，第二电极312位于正中矢状面另一侧的人体髂骨处。又例如，当用户佩戴可穿戴设备时，第一电极311位于正中矢状面一侧的人体腹部，第二电极312位于正中矢状面另一侧的人体后腰处。此外，第一电极311和第二电极312的材料、尺寸、对皮肤的压力等可以一致，也可以不一致。
在一些实施例中，如图7中的图(a)和图(b)所示，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别贴合在人体的正中矢状面两侧的髂骨701位置。作为示例性说明，人体的正中矢状面可以将髂骨701分为左侧髂骨7011和右侧髂骨7012，第一电极311和第二电极312可以分别与覆盖在左侧髂骨7011和右侧髂骨7012对应的皮肤区域贴合，以采集人体的心电信号。在一些实施例中，当人体佩戴穿戴结构320时，第一电极311和第二电极312分别与覆盖在左侧髂骨7011和右侧髂骨7012的皮肤区域的贴合位置可以关于人体的正中矢状面对称，这样可以提高第一电极311和第二电极312贴合位置的运动伪迹的一致性，从而有利于对运动伪迹进行消除，提高心电信号的质量。在一些实施例中，第一电极311和第二电极312分别与覆盖在左侧髂骨7011和右侧髂骨7012的皮肤区域的贴合位置也可以关于人 体的正中矢状面非对称。
在一些实施例中，如图7中的图(a)所示，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别贴合在人体的正中矢状面两侧的髂前上棘702位置。其中，髂前上棘702可以包括分别位于左侧髂骨7011和右侧髂骨7012上的左侧髂前上棘7021和右侧髂前上棘7022。作为示例性说明，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别与覆盖在左侧髂前上棘7021和右侧髂前上棘7022对应的皮肤区域贴合，以采集人体的心电信号。由于左侧髂前上棘7021和右侧髂前上棘7022处的肌肉较少，即使人体处于运动状态时，肌电信号对第一电极311和第二电极312所采集的心电信号干扰也较少，可以保证心电信号具有较好的质量。左侧髂前上棘7021和右侧髂前上棘7022关于人体的正中矢状面对称，第一电极311和第二电极312对应的运动伪迹具有较好的一致性，有利于对运动伪迹进行消除，提高心电信号的质量。此外，左侧髂前上棘7021和右侧髂前上棘7022距离心脏的距离差较大，两者的电位差也较大，第一电极311和第二电极312分别与覆盖在左侧髂骨7011和右侧髂骨7012对应的皮肤区域贴合，可以提高所采集到的心电信号的强度。除此之外，左侧髂前上棘7021和右侧髂前上棘7022具有凸起，以便于与第一电极311和第二电极312充分贴合，保证第一电极311和第二电极312不易脱落，并且人体对于左侧髂前上棘7021和右侧髂前上棘7022处对应的皮肤区域敏感交底，可以保证人体在佩戴穿戴结构320时，具有较好的穿戴舒适感。
在一些实施例中，如图7中的图(b)所示，第一电极311和第二电极312可以分别贴合在人体的正中矢状面两侧的髂后上棘703位置。其中，髂后上棘703可以包括分别位于左侧髂骨7011上的左侧髂后上棘7031和右侧髂骨7012上的右侧髂后上棘7032。作为示例性说明，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别与覆盖在左侧髂后上棘7031和右侧髂后上棘7032对应的皮肤区域贴合，以采集人体的心电信号。由于左侧髂后上棘7031和右侧髂后上棘732处的肌肉较少，即使人体处于运动状态时，肌电信号对第一电极311和第二电极312所采集的心电信号干扰较少，可以保证心电信号具有较好的质量，并且左侧髂后上棘7031和右侧髂后上棘7032关于人体的正中矢状面对称，运动伪迹具有较好的一致性，有利于对运动伪迹进行消除，提高心电信号的质量。
在一些实施例中，如图6中的图(b)所示，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别贴合在人体的正中矢状面两侧的后腰602位置。作为示例性说明，人体的正中矢状面将后腰602分为了左侧后腰6021和右侧后腰6022，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别与左侧后腰6021和右侧后腰6022对应的皮肤区域贴合，以采集心电信号。在一些实施例中，当人体佩戴穿戴结构320时，第一电极311和第二电极312分别与左侧后腰6021和右侧后腰6022的皮肤区域的贴合位置可以关于人体的正中矢状面对称，这样可以提高第一电极311和第二电极312贴合位置的运动伪迹的一致性，从而有利于对运动伪迹进行消除，提高心电信号的质量。在一些实施例中，第一电极311和第二电极312分别与左侧后腰6021和右侧后腰6022的皮肤区域的贴合位置可以关于人体的正中矢状面不对称。
在一些实施例中，如图6中的图(a)所示，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别与在人体的正中矢状面两侧的腹部603贴合。作为示例性说明，人体的正中矢状面将腹部603分为了左侧腹部6031和右侧腹部6032，当人体佩戴穿戴结构320时，第一电极311和第二电极312可以分别与左侧腹部6031和右侧腹部6032的皮肤区域贴合，以采集心电信号。在一些实施例中，当人体佩戴穿戴结构320时，第一电极311和第二电极312分别与左侧腹部6031和右侧腹部6032的皮肤区域的贴合位置可以关于人体的正中矢状面对称，这样可以提高第一电极311和第二电极312贴合位置的运动伪迹的一致性，从而有利于对运动伪迹进行消除，提高心电信号的质量。在一些实施例中，第一电极311和第二电极312分别与左侧腹部6031和右侧腹部6032的皮肤区域的贴合位置可以关于人体的正中矢状面不对称。
图8是根据本说明书一些实施例所示的可穿戴设备的结构示意图。
在一些实施例中，如图8所示，可穿戴设备800可以包括第一电极811、第二电极812、参考电极813以及穿戴结构820，第一电极811和第二电极812可以间隔设置于穿戴结构820靠近人体皮肤的表面，参考电极813可以位于第一电极811和第二电极812之间。其中，第一电极811、第二电极812以及穿戴结构820与可穿戴设备300中的第一电极311、第二电极312以及穿戴结构320类似，关于第一电极811、第二电极812以及穿戴结构820的更多描述可以参考第一电极311、第二电极312以及穿戴结构320的相关描述，在此不再进行赘述。
在一些实施例中，当人体佩戴穿戴结构820时，第一电极811和第二电极812可以分别贴合人体的正中矢状面的两侧，参考电极813可以与人体的皮肤贴合。在一些实施例中，参考电极813可以为第一电极811和第二电极812提供参考地电压，便于后续利用电路(例如，差分放大器)对第一电极811对应的第一电位和第二电极812对应的第二电位进行处理。在一些实施例中，参考电极813也可以与右腿驱动 电路电连接，右腿驱动电路可以降低参考电极813到人体的阻抗，使得右腿驱动电路可以更加有效地降低工频干扰。在一些实施例中，当人体穿戴可穿戴设备800时，参考电极813可以位于人体腰部区域的后腰侧、腹部等其它位置。优选地，当人体佩戴可穿戴设备800时，第一电极811和第二电极812相对于人体正中矢状面对称设置，相应地，参考电极813可以位于人体腰部区域后腰侧的中央(例如，腰椎及其附近区域)，可以使得用户在穿戴可穿戴设备800时，各电极相对于人体正中矢状面是对称设置的，使得第一电极811对应的第一电位与参考电极813的电位差的数值与第二电极812对应的第二电位与参考电极813的电位差的数值大致相等，以便于后续电路(例如，差分放大器)的处理。此外，人体后腰侧的肌肉较少，肌电信号对心电信号的影响较小，并且人体后腰侧对外界物体或刺激的感知敏感度较低，可以保证用户在使用可穿戴设备800时可以具有较为舒适的佩戴体验。需要说明的是，参考电极813的数量不限于图8中所示的一个，还可以为多个。例如，参考电极813可以包括第一参考电极和第二参考电极，当人体穿戴可穿戴设备800时，第一参考电极和第二参考电极可以位于人体腰部区域的左后腰和右后腰处，其中，第一参考电极和第二参考电极可以关于人体正中矢状面对称设置，以使得人体穿戴可穿戴设备800时，可穿戴设备800上的各电极可以关于人体正中矢状面对称设置。在一些实施例中，第一参考电极和第二参考电极也可以不关于人体正中矢状面对称设置。例如，第一参考电极和第二参考电极均位于人体的左后腰或右后腰处。又例如，第一参考电极位于人体左后腰远离人体正中矢状面的位置，第二参考电极位于人体右后腰靠近人体正中矢状面的位置。在一些实施例中，第一参考电极和第二参考电极之间通过导线连接，以使得两个参考电极为同一电势，此时，第一参考电极和第二参考电极可以作为当一个参考电极，从而提高可穿戴设备800关于对称性设置的自由度。此外，第一参考电极和第二参考电极不限于设置于人体腰部区域的后腰侧，还可以位于腹部、左右腰侧、左右臀部、左右大腿等其他区域。
在一些实施例中，参考电极813沿穿戴结构820的第二延伸方向的尺寸可以不小于第一电极811或第二电极812沿穿戴结构820的第二延伸方向的尺寸，以便参考电极813与人体进行贴合。在一些实施例中，参考电极813在第二延伸方向的尺寸可以在5mm～80mm的范围内。在一些实施例中，参考电极813在第二延伸方向的尺寸可以在10mm～80mm的范围内。在一些实施例中，参考电极813在第二延伸方向的尺寸可以在20mm～80mm的范围内。在一些实施例中，参考电极813在第二延伸方向的尺寸可以在30mm～80mm的范围内。需说明的是，参考电极813沿穿戴结构820的第二延伸方向的尺寸也可以小于第一电极811或第二电极812沿穿戴结构820的第二延伸方向的尺寸，能够提供参考地电压即可。
图9是根据本说明书一些实施例所示的裤装的正面结构示意图。如图9所示，可穿戴设备可以是裤装900。裤装900可以包括第一应变传感器910、第一电极921、第二电极922、参考电极923以及第二应变传感器930。
在一些实施例中，第一应变传感器910位于穿戴结构上，并与人体的腰部区域相贴合，第一应变传感器910基于人体呼吸时腰部区域的起伏变化采集人体的呼吸状态信息。
在一些实施例中，第一应变传感器910可以是围绕用户腰部的带状/环状结构。例如，第一应变传感器910可以是具有弹性，可以围绕腰部延展的腰带状传感器。用户穿戴裤装900后进行吸气运动时，胸廓周向围度扩大，膈顶下降，引起第一应变传感器910周向围度变化。第一应变传感器910可以测量上述周向围度变化情况。同样地，用户穿戴裤装900后进行呼气运动时，胸廓周向围度缩小，膈顶上升，第一应变传感器910可以测量此时的周向围度变化情况。将上述吸气运动与呼气运动周向围度变化的过程作为一个呼吸过程，得到人体的呼吸状态信息。在一些实施例中，第一应变传感器910也可以是不围绕人体腰部设置。例如，第一应变传感器910可以贴合在人体的腹部区域，当人体呼吸时，腹部区域会发生起伏变化，第一应变传感器910基于腹部区域的起伏变化发生形变，从而获取用户的呼吸状况信息。在一些实施例中，第一应变传感器910的数量可以为一个或多个，当第一应变传感器910的数量为多个时，多个第一应变传感器910可以与腹部区域的不同区域进行贴合，从而获取更加准确的呼吸状况信息。在一些实施例中，第一应变传感器910可以包括应变式压力传感器、应变式扭矩传感器、应变式位移传感器、应变式加速度传感器等中的任意一种或多种。在一些实施例中，第一应变传感器910也可以替换为气压传感器，示例性地，气压传感器可以包括密闭的柔性腔体和压力传感元件，气压传感器与柔性腔体密封连接。当人体佩戴气压传感器时，气压传感器与人体的腰部区域或腹部相贴合，人体呼吸时，腰部区域或腹部区域的起伏可以作用于气压传感器的柔性腔体，柔性腔体的内部压强发生变化，压力传感元件将压强变化转化为电信号，以获取人体呼吸时腰部区域或腹部区域的起变化，从而确定人体的呼吸状况信息。在一些实施例中，气压传感器的数量可以为一个或多个，当气压传感器的数量为多个时，多个气压传感器可以分布于人体腰部区域或腹部区域的不同位置，以获取腰部区域或腹部区域不同位置的起伏情况。
呼吸状态信息可以反映人体呼吸过程的生理结构变化。在一些实施例中，人体的呼吸状态信息可以包括呼吸频率、呼吸深度、呼入时间、呼出时间、呼吸的平稳性等。其中，呼吸频率、呼入时间、呼出时间可以通过测量上述周向围度变化的时间确定。呼吸深度可以通过测量上述周向围度变化的峰值(如周 向围度最大值、周向围度最小值)确定。呼吸的平稳性可以通过测量多个呼吸过程并比较多个呼吸过程之间的呼吸深度、呼入时间、呼出时间以确定。通过第一应变传感器910可以实现呼气过程的实时监控。
第一电极921、第二电极922可以参考图3、图4、图5中的第一电极311和第二电极312，或参考图8中第一电极811、第二电极812。在一些实施例中，至少两个电极(第一电极921、第二电极922)间隔分布于穿戴结构上，当人体佩戴穿戴结构裤装900时，至少两个电极(第一电极921、第二电极922)位于人体的正中矢状面的两侧，也就是说，第一电极921可以位于人体正中矢状面的左侧，第二电极922可以位于人体正中矢状面的右侧。例如，第一电极921可以位于人体正中矢状面的左腿侧，第二电极922可以位于人体正中矢状面的右腿侧。优选地，当人体佩戴穿戴结构裤装900时，至少两个电极(第一电极921、第二电极922)位于人体腰部区域的正中矢状面的两侧。例如，第一电极921可以位于人体腰部区域的正中矢状面左侧，第二电极922可以位于人体腰部区域的正中矢状面右侧。为了与人体皮肤紧密贴合，第一电极921、第二电极922可以位于裤装900内侧，即靠近人体皮肤一侧。在一些实施例中，第一电极921、第二电极922可以通过魔术贴、胶黏、卡扣、网袋等方式与裤装900连接。第一电极921、第二电极922可以获取心电信号。为了降低肌电信号的干扰，在一些实施例中，当人体佩戴穿戴结构裤装900时，第一电极921和第二电极922分别贴合在人体的正中矢状面两侧的髂骨、后腰或腹部。第一电极921和第二电极922贴合在人体的正中矢状面两侧的后腰可以参见图10。
在一些实施例中，当人体佩戴穿戴结构裤装900时，第一电极921和第二电极922关于人体的正中矢状面对称设置。电极对称设置可以使得第一电极921和第二电极922所贴合的位置的运动伪迹具有较好的一致性，有利于运动伪迹的消除，例如，可以通过差分放大电路来消除心电信号中的运动伪迹，以提高心电信号的质量。
参考电极923可以提供参考地电压。在一些实施例中，第一电极921和第二电极922间隔设置于穿戴结构裤装900靠近人体皮肤的表面，参考电极923并位于第一电极921和第二电极922之间。参考电极923也可以位于裤装900内侧。参考电极923可以参考图8中的参考电极813。
第二应变传感器930可以用于获取人体下肢动作参数。第二应变传感器930可以感应人体关节的弯曲、伸展运动。示例性的下肢动作参数可以包括关节弯曲角度、弯曲方向等。在一些实施例中，第二应变传感器930可以位于穿戴结构裤装900与人体腿部或臀部对应的位置处。图9仅绘制第二应变传感器930在左膝关节外侧处的情况，可以理解的是，第二应变传感器930可以位于人体下肢任何一个关节，如右膝关节、踝关节、髋关节处等；第二应变传感器930可以位于人体下肢任何一个关节的任意位置处，如左膝关节外侧处、左膝关节内侧处等。第二应变传感器930位于穿戴结构裤装900与人体臀部对应的位置处可以参见图10。在一些实施例中，第二应变传感器930可以是多个，并分别位于上述不同的下肢关节处。
在一些实施例中，处理器进一步被配置为基于第二应变传感器930识别人体下肢的运动动作。例如，第二应变传感器930获取膝关节弯曲角度为180°、髋关节弯曲角度为120°时，处理器判断下肢运动动作为下蹲；膝关节弯曲角度为0°、髋关节弯曲角度为0°时，处理器判断下肢运动动作为站立。通过第二应变传感器930可以实现人体下肢动作的实时监测。
图10是根据本说明书一些实施例所示的裤装的背面结构示意图。
如图10所示，第一应变传感器910可以与人体的腰部区域相贴合。第一电极921和第二电极922贴合在人体的正中矢状面两侧的后腰。其中，第一电极921贴合在人体的正中矢状面两侧的右后腰，第二电极922贴合在人体的正中矢状面两侧的左后腰。第二应变传感器930位于穿戴结构裤装900与人体臀部(右臀部)对应的位置处。可以理解的是，上述组件的位置仅为说明，在原理相同情况下，上述组件可以位于人体下肢任意位置。例如，第一电极921、第二电极922除了设置于腰部位置，还可以设置于大腿、小腿、膝关节、脚踝等位置。
图11是根据本说明书一些实施例所示的一个标准的心电图，图11中示出的波形1101显现出了P波、QRS波群、ST段、T波、P-R间期、U波等特征。
在一些实施例中，处理器可以基于心电信号确定人体的心率变异性和心率。至少两个电极获取的心电信号可以如图11所示。在一些实施例中，心率可以表示为单位时间(例如，一分钟)内R波出现的次数，例如，通过60除以P-P间期或60除以R-R间期计算得到。
在一些实施例中，处理器可以通过统计学分析，计算一个或多个有关R-R间期的数理统计指标以确定心率变异性。其中，心率异变性可以以SDNN、SDANN、RMSSD、pNN50、SDNNi等统计指标呈现。例如，心率变异性可以通过如下公式(1)计算得到：
其中，SDNN为相邻心跳间隔的标准差；N为数量；RRsi为RR间期；i为不同的RR间期；SDNN可以反 映交感神经与副交感神经总的张力大小。SDNN的正常值为100毫秒至150毫秒，SDNN的异常值为小于50毫秒。
再例如，心率变异性可以通过如下公式(2)、公式(3)计算得到：

其中，SDANN为计算每5分钟RR间期的平均值，再计算得到若干间期的标准差；RRs5为5分钟内RR间期的平均值；SDANN反映交感神经张力大小，与心率的缓慢变化成分相关，当交感神经张力增高时，其值降低。SDANN的正常值为80毫秒至140毫秒，SDANN的异常值为小于50毫秒。
还例如，心率变异性可以通过如下公式(4)计算得到：
其中，RMSSD为相邻心跳间隔的均方根值。RMSSD可以反映副交感神经张力大小，与心率的快速变化成分相关，当副交感神经张力降低时，其值降低。RMSSD的正常值为15毫秒至45毫秒，RMSSD的异常值为小于15毫秒。
还例如，心率变异性可以通过如下公式(5)计算得到：
其中，pNN50为相邻心跳间隔差值超过50毫秒的比例。pNN50反映副交感神经张力大小，与心率的快速变化成分相关，当副交感神经张力降低时，其值降低。pNN50的正常值为1％至12％，pNN50的异常值为小于0.75％。
还例如，心率变异性可以通过如下公式(6)、公式(7)计算得到：

其中，SDNNi为24小时记录中，每5分钟的心跳间隔标准差的平均值；SDNNs5为每5分钟的心跳间隔标准差。SDNNi反映交感神经张力大小，与心率的缓慢变化成分相关，当交感神经张力增高时，其值降低。SDNNi的正常值为40毫秒至80毫秒，SDNNi的异常值为小于20毫秒。可以理解的是，确定心率变异性的数理统计指标还可以是其他数理统计指标，本说明书对此不做限定。
在一些实施例中，处理器可以对心电信号中R-R间期的分布进行分析，得到R-R间期的变异度作为心率变异性。例如，通过R-R间期直方图、R-R间期差值直方图、HRV的时间序列图等得到R-R间期的变异度作为心率变异性。
在一些实施例中，处理器可以通过频域分析确定心率变异性。例如，通过全部正常心跳间期所有频率范围功率总和来评估整体心率变异性；低频范围内的功率表征交感神经和副交感神经活性；高频范围内的功率表征副交感神经活性等。
在一些实施例中，处理器可以通过非线性分析法确定心率变异性。例如，通过分析分维数(相关维、Hausdorf维或信息维)分析法、复杂度分析法、Lyapaunov指数、哥式(Kolmogorov)熵。近似熵分析等确定心率变异性。
在一些实施例中，处理器可以通过心电散点图的指数确定心率变异性。例如，通过矢量长度指数、矢量角度指数等确定心率变异性。
在一些实施例中，处理器可以采用特定的数学算法，建立相应的数学模型确定心率变异性。例如，通过分维数、测度熵、Lyapunov指数、复杂度等参数确定心率变异性。
图12是根据本说明书一些实施例所示的根据心率变异性确定人体的身体状态的示意图。
在一些实施例中，身体状态可以包括放松状态和紧张状态。其中，放松状态可以指人体在清醒、安静、情绪平静、微量运动/未运动时的生理状态。仅作为示例性说明，放松状态可以表现为静息心率在60至80次/分钟；心率变异性相关的数理统计指标为正常值(大于预设阈值)。紧张状态可以指人体在焦虑、情绪波动、进行定量运动时的生理状态。紧张状态可以表现为心率在80至100次/分钟；心率变异性相关的数理统计指标为异常值(不大于预设阈值)。人体处于放松情况下，心脏每次跳动的时间间隔差异大，表现为心率变异性较大。而人体处于高度紧张情况下，心脏每次跳动的时间间隔差异小，表现为心率变异性较小。在一些实施例中，处理器可以在心率变异性不大于预设阈值时，确定人体运动时的身体状态为紧张状态；心率变异性大于预设阈值时，确定人体运动时的身体状态为放松状态。其中，心率变异性的预设阈值可以通过经验数值确定。如心率变异性的数理统计指标为SDNN时，心率变异性的预设阈值可以是50 毫秒。
在一些实施例中，处理器可以通过完成训练的机器学习模型，至少以心率变异性作为输入数据，机器学习模型基于输入数据输出人体的身体状态。示例性的机器学习模型可以包括深度神经网络模型、卷积神经网络模型等。在一些实施例中，机器学习模型可以通过大量带有标识的训练样本训练得到。具体的，将带有标识的多组训练样本输入初始模型，基于初始模型的输出以及标识构建损失函数，基于损失函数迭代通过训练更新模型的参数。在一些实施例中，可以基于训练样本，通过各种方法进行训练。例如，可以基于梯度下降法进行训练。当满足预设条件时，训练结束，获得训练好的模型。其中，预设条件可以为损失函数收敛。
在一些实施例中，训练样本可以包括多个历史心率变异性数据(如SDNN、SDANN、RMSSD、pNN50、SDNNi等统计指标)。标识可以是每个历史心率变异性数据对应的人体的身体状态。通过机器学习模型可以实现人体的身体状态智能识别，提高识别效率，减少因主观判断身体状态而产生的判断结果的误差。
在一些实施例中，处理器可以获取标定曲线，并根据标定曲线确定人体的身体状态。其中，标定曲线根据对人体处于不同身体状态下所产生的心率变异性拟合获得。例如，标定曲线可以通过对心率变异性的数理统计指标SDNN、SDANN、RMSSD、pNN50、SDNNi等拟合获得。在一些实施例中，标定曲线的横坐标可以是时间，纵坐标可以是心率变异性各个数理统计指标的值。在一些实施例中，处理器可以将标定曲线中满足正常值范围的曲线段作为放松状态的区间，将不满足正常值范围的曲线段作为紧张状态的区间。例如，对于数理统计指标SDNN，其标定曲线中满足SDNN值为100毫秒至150毫秒的曲线段作为放松状态的区间，满足SDNN值为小于50毫秒的曲线段作为紧张状态的区间。
图13是根据本说明书一些实施例所示的根据心率变异性以及心率确定人体的身体状态的示意图。
心率也可以反映人体的身体状态，为了进一步提高可穿戴设备对人体的身体状态的判断精准性，在一些实施例中，处理器可以根据心率变异性和心率确定人体的身体状态。
在一些实施例中，处理器可以在心率变异性不大于预设阈值且心率大于预设心率阈值时，确定人体运动时的身体状态为紧张状态；在心率变异性大于预设阈值且心率不大于预设心率阈值时，确定人体运动时的身体状态为放松状态。其中，心率变异性的预设阈值可以通过经验数值确定。如心率变异性的数理统计指标为SDNN时，心率变异性的预设阈值可以是50毫秒。心率的预设心率阈值可以通过经验值或用户自身的身体健康状况进行设置。例如，预设心率阈值可以为60次/分钟、70次/分钟、80次/分钟等。
在一些实施例中，处理器可以通过完成训练的机器学习模型，至少以心率变异性、心率作为输入数据，机器学习模型基于输入数据输出人体的身体状态。示例性的机器学习模型可以包括深度神经网络模型、卷积神经网络模型等。在一些实施例中，机器学习模型可以通过大量带有标识的训练样本训练得到。具体的，将带有标识的多组训练样本输入初始模型，基于初始模型的输出以及标识构建损失函数，基于损失函数迭代通过训练更新模型的参数。在一些实施例中，可以基于训练样本，通过各种方法进行训练。例如，可以基于梯度下降法进行训练。当满足预设条件时，训练结束，获得训练好的模型。其中，预设条件可以为损失函数收敛。
在一些实施例中，训练样本可以包括多个历史心率变异性数据(如SDNN、SDANN、RMSSD、pNN50、SDNNi等统计指标)以及多个历史心率数据。标识可以是每个历史心率变异性数据以及历史心率数据对应的人体的身体状态。通过机器学习模型可以实现人体的身体状态智能识别，提高识别效率，减少因主观判断身体状态而产生的判断结果的误差；另外，模型输入中加入心率数据，增加了模型的计算围度，提高与实际生理情况的拟合程度。
图14是根据本说明书一些实施例所示的根据心率变异性以及呼吸状态信息确定人体的身体状态的示意图。
呼吸也可以反映人体的身体状态，为了进一步提高可穿戴设备对人体的身体状态的判断精准性，在一些实施例中，处理器可以根据心率变异性和呼吸状态信息确定人体的身体状态。
在一些实施例中，处理器可以在心率变异性大于预设阈值且呼吸状态信息在预设呼吸状态信息范围内时，确定人体运动时的身体状态为放松状态；心率变异性不大于预设阈值且呼吸状态信息未在所述预设呼吸状态信息范围内时，确定人体运动时的身体状态为紧张状态。其中，心率变异性的预设阈值可以通过经验数值确定。如心率变异性的数理统计指标为SDNN时，心率变异性的预设阈值可以是50毫秒。仅作为示例，呼吸状态信息可以包括呼吸频率、呼吸深度、呼入时间、呼出时间、呼吸的平稳性等。呼吸状态信息可以反映人体的身体状态。例如，当人体的呼吸频率正常、呼吸深度较大、呼入时间和呼出时间较长、呼吸过程较为稳定时，人体可以被判断为放松状态；当人体的呼吸频率异常(如呼吸频率过快)、呼吸深度较小、呼入时间和呼出时间较短、呼吸过程不稳定时，人体可以被判断为紧张状态。预设呼吸状态信息范围可以通过经验值确定。如呼吸状态信息为呼吸频率时，预设呼吸状态信息范围可以是12次/分钟至20 次/分钟等。进一步地，预设呼吸状态信息范围还可以是呼吸深度、呼入时间、呼出时间、呼吸的平稳性等相对应的预设范围。
在一些实施例中，处理器可以基于心率变异性以及呼吸状态信息中的至少一个参数，确定人体的身体状态。在一些实施例中，呼吸状态信息可以包括呼吸频率，处理器可以基于心率变异性以及呼吸频率确定人体的身体状态。在一些实施例中，基于心率变异性以及呼吸频率确定人体的身体状态可以包括：当人体的身体状态心率变异性不大于预设阈值且呼吸频率大于预设呼吸频率范围的上限时，用户的身体状态为紧张状态；当人体的身体状态心率变异性不大于预设阈值且呼吸频率在预设呼吸频率范围时，用户的身体状态为放松状态。在一些实施例中，呼吸状态信息可以包括呼吸深度，处理器可以基于心率变异性以及呼吸深度确定人体的身体状态。在一些实施例中，基于心率变异性以及呼吸深度确定人体的身体状态可以包括：当人体的身体状态心率变异性不大于预设阈值且呼吸频率小于预设呼吸深度范围的下限时，用户的身体状态为紧张状态；当人体的身体状态心率变异性不大于预设阈值且呼吸深度在预设呼吸深度范围时，用户的身体状态为放松状态。在一些实施例中，呼吸状态信息可以包括呼入时间，处理器可以基于心率变异性以及呼入时间确定人体的身体状态。在一些实施例中，基于心率变异性以及呼入时间确定人体的身体状态可以包括：当人体的身体状态心率变异性不大于预设阈值且呼入时间小于预设呼入时间范围的下限时，用户的身体状态为紧张状态；当人体的身体状态心率变异性不大于预设阈值且呼入时间在预设呼入时间范围时，用户的身体状态为放松状态。在一些实施例中，呼吸状态信息可以包括呼出时间，处理器可以基于心率变异性以及呼出时间确定人体的身体状态。在一些实施例中，基于心率变异性以及呼出时间确定人体的身体状态可以包括：当人体的身体状态心率变异性不大于预设阈值且呼出时间小于预设呼出时间范围的下限时，用户的身体状态为紧张状态；当人体的身体状态心率变异性不大于预设阈值且呼出时间在预呼出时间出范围时，用户的身体状态为放松状态。在一些实施例中，呼吸状态信息可以包括呼吸稳定性，其中，呼吸稳定性可以通过呼吸频率、呼吸深度、呼入时间和呼出时间进行计算，处理器可以基于心率变异性以及呼吸的平稳性确定人体的身体状态等。需要说明的是，处理器还可以基于心率变异性以及呼吸状态信息中的多个参数确定人体的身体状态，此处不再赘述。
在一些实施例中，处理器可以通过完成训练的机器学习模型，至少以心率变异性和呼吸状态信息作为输入数据，机器学习模型基于输入数据输出人体的身体状态。示例性的机器学习模型可以包括深度神经网络模型、卷积神经网络模型等。在一些实施例中，机器学习模型可以通过大量带有标识的训练样本训练得到。具体的，将带有标识的多组训练样本输入初始模型，基于初始模型的输出以及标识构建损失函数，基于损失函数迭代通过训练更新模型的参数。在一些实施例中，可以基于训练样本，通过各种方法进行训练。例如，可以基于梯度下降法进行训练。当满足预设条件时，训练结束，获得训练好的模型。其中，预设条件可以为损失函数收敛。
在一些实施例中，训练样本可以包括多个历史心率变异性数据(如SDNN、SDANN、RMSSD、pNN50、SDNNi等统计指标)以及多个历史呼吸状态信息(如历史呼吸频率、历史呼吸深度、历史呼入时间、历史呼出时间、历史呼吸的平稳性等中的任意一种或多种)。标识可以是每个历史心率变异性数据以及历史呼吸状态信息对应的人体的身体状态。通过机器学习模型可以实现人体的身体状态智能识别；另外，模型输入中加入呼吸状态信息，增加了模型的计算围度，提高模型与实际生理情况的拟合程度。可以理解的是，心率变异性、心率、呼吸状态信息等参数可以同时作为机器学习模型的输入，以进一步提高模型与实际生理情况的拟合程度。
在一些实施例中，不同运动动作对应不同的心率变异性，处理器进一步被配置为基于心率变异性和运动动作评估所述运动动作。运动动作可以基于图9、图10中的第二应变传感器930确定。
在一些实施例中，处理器、终端设备或数据库可以预先存储至少一个参考动作的动作参数以及心率变异性数据。当处理器基于第二应变传感器930获取的人体下肢动作参数确定当前运动动作后，处理器可以将当前运动动作的动作参数与参考动作的动作参数进行比较，将当前运动动作的心率变异性与参考动作的心率变异性进行比较，当二者相似度大于预设相似度阈值时，则评估结果为当前运动动作符合参考动作的标准，也可理解为，当前运动动作正确。当二者相似程度小于预设相似度阈值时，则评估结果为当前运动动作不符合参考动作的标准，可以理解为，当前运动动作错误或者不标准。例如，处理器可以预存瑜伽、普拉提、冥想、打坐、站桩、呼吸训练等运动的关节弯曲角度、弯曲方向等动作参数，以及上述运动对应的心率变异性，如果当前的关节弯曲角度、弯曲方向与参考动作的关节弯曲角度、弯曲方向拟合程度不小于第一阈值(例如，90％)，且当前心率变异性和参考心率变异性拟合程度不小于第二阈值(例如，90％)，评估结果为当前运动动作符合参考动作的标准。如果当前的关节弯曲角度、弯曲方向与参考动作的关节弯曲角度、弯曲方向拟合程度小于第一阈值，且当前心率变异性和参考心率变异性拟合程度小于预设阈值时，则评估结果为当前运动动作不符合参考动作的标准。
在一些实施例中，处理器可以通过反馈模块向用户发送反馈信息。例如，响应于评估结果为当前 运动动作不符合参考动作的标准，处理器可以通过反馈模块向用户反馈具体的动作参数，并给出指导方案、锻炼方案等。
当用户处于紧张状态时，用户通常会通过相关运动(例如，瑜伽、普拉提、冥想、打坐、站桩、呼吸训练)以调节自身的心率变异性，在一些实施例中，处理器进一步被配置为基于心率变异性和运动动作，可以判断运动动作是否达到预定效果。预定效果是指用户的心率变异性不小于预设阈值。示例性地，当用户的心率变异性不小于预设阈值时，则运动动作达到预定效果。当用户的心率变异性小于预设阈值时，则运动动作未能达到预期效果，处理器可以通过反馈模块提示用户继续进行当前运动动作或更换运动动作，直至用户的心率变异性小于预设阈值。
图15是根据本说明书一些实施例所示的另一可穿戴设备的示例性结构图。如图15所示，可穿戴设备1500可以包括至少两个电极210、穿戴结构220、处理器230、反馈模块240以及肌电模块250。至少两个电极210、穿戴结构220、处理器230可以参见图2的描述。
在一些实施例中，可穿戴设备还可以包括反馈模块240。反馈模块与处理器通信连接，处理器响应于人体的身体状态发出控制指令，反馈模块基于控制指令向用户发出反馈信息。控制指令可以用于确定反馈模块240是否发送反馈信息，以及反馈模块240发送反馈信息的具体内容。示例性的反馈信息的具体内容可以包括训练计划、动作提示、健康提醒等。
在一些实施例中，反馈模块240可以被配置为扬声器，扬声器基于控制指令控制扬声器进入工作状态或切换音频信号。例如，当人体的身体状态为紧张状态时，控制指令可以为确定反馈模块240发送反馈信息，以及切换音频信号为曲调舒缓的音乐；当人体的身体状态为放松状态时，控制指令可以不发送反馈信息。音频信号可以通过网络获取或通过预先存储在数据库中获取。
在一些实施例中，反馈模块240可以被配置为其他设备。例如，电子屏、振动按摩装置等。
在一些实施例中，扬声器进入状态或切换音频信号后，处理器继续至少基于心电信号判断人体的身体状态，若人体的身体状态仍处于紧张状态，处理器可以切换音频信号，直至人体的身体状态进入放松状态。处理器可以通过电极、第一应变传感器以及第二应变传感器实时监控人体的身体状态，并基于实时监控的结果确定反馈信息。例如，当反馈模块240播放一段舒缓音乐后，人体的身体状态仍处于紧张状态，则处理器可以切换曲目，直至所述人体的身体状态进入放松状态。
在一些实施例中，如果用户在进行目标动作时身体状态为紧张状态，反馈模块240还可以引导用户进行动作切换以将用户从紧张状态调整为放松状态，用户身体状态调整至放松状态之后，反馈模块240提醒用户继续进行之前的目标动作。具体地，当处理器判断用户的身体状态为紧张状态，处理器控制反馈模块240向用户发出反馈信息，该反馈信息至少包括提示用户切换动作的信息。例如，用户在进行瑜伽时，处理器判断用户的身体状态为紧张状态，处理器控制反馈模块240向用户发出语音提醒，提醒用户身体处于紧张状态，建议进行打坐或站桩动作对身体状态进行调整，此外反馈模块240还可以引导用户呼吸，以进一步提高用户身体状态的调整，待用户的身体调整至放松状态时，并提示用户继续进行之前的瑜伽动作。在一些实施例中，反馈模块240引导用户进行动作切换的方式可以包括语音提醒、文字信息、视频信息、振动、电击等其中的任意一种或几种。
在一些实施例中，人体的身体状态与反馈信息相关联。当人体的身体状态为紧张状态时，反馈信息可以是缓解紧张状态的信息。例如，舒缓音乐、令人舒适的音频、风景画面、指导信息(例如，包括指导调整呼吸的信息、指导调整动作的信息等)等。在一些实施例中，人体的运动动作与反馈信息相关联。例如，当人体的运动动作为瑜伽时，反馈信息可以包括对瑜伽动作的指导、训练计划、健康提醒等信息。
在一些实施例中，可穿戴设备还可以包括肌电模块250。
肌电模块250可以用于采集人体的肌电信号。在一些实施例中，处理器可以基于肌电信号、运动动作以及呼吸状态信息、心率或心率变异性中的一种或几种判断人体的运动动作是否标准。
肌电信号可以从人体很多部位获取，比如小腿、大腿、臀部、腰、后背、胸部、肩部、手臂、颈部等，从不同部位获取的肌电信号携带着相应部位的运动和功能信息。例如，腿上的肌电信号反应腿部的姿势和运动状态，如行走、跑步、蹲下等。因此，可以通过可穿戴设备采集肌电信号来满足人们对运动健身指导的要求。例如，当人体在进行力量训练时，以哑铃侧平举为例，人体双臂伸直向两侧张开时，肌肉(尤其是肩部肌肉)发力，肌电信号明显增强，人体一般处于吸气状态；双臂合拢，肌电信号减弱，用户一般处于呼气状态，这里可以通过判断人体在不同呼吸状态下肩部肌肉的肌电信号的强弱来对人体侧平举动作中的各个子动作进行评估，从而判断各个子动作是否标准。
在一些实施例中，处理器、终端设备或数据库可以预先存储至少一个参考动作的肌电信号、运动动作以及呼吸状态信息、心率或心率变异性中的一种或几种。处理器可以将当前运动动作的上述参数与参考动作的上述参数进行比较，当相似度均大于预设相似度阈值时，评估结果为当前运动动作符合参考动作的标准，即，用户的运动动作正确；当至少一个参数的相似程度小于预设相似度阈值时，则评估结果为当 前运动动作不符合参考动作的标准，及用户当前的运动动作错误或不标准。
本说明书实施例可能带来的有益效果包括但不限于：(1)通过可穿戴设备的各个组件对人体的心率变异性、心率、呼吸状态信息、运动动作的实时监控，实现人体的紧张/放松状态的实时监控；(2)电极、应变传感器等监控组件集成在可穿戴设备上，通过结构、面料的设计减少上述监控组件对穿戴舒适度的影响；(3)通过机器学习模型将心率变异性、心率、呼吸状态信息等参数作为模型输入，增加模型的输入围度，提高模型与实际生理情况的拟合程度；(4)基于紧张/放松状态监控结果，以及运动动作的判断结果进行不同内容的反馈，提高用户在不同使用场景下的使用体验；(5)可穿戴设备能够应用于体育锻炼、心理测试、临床健康监控、运动动作矫正、康复治疗等多种应用场景。需要说明的是，不同实施例可能产生的有益效果不同，在不同的实施例里，可能产生的有益效果可以是以上任意一种或几种的组合，也可以是其他任何可能获得的有益效果。
上文已对基本概念做了描述，显然，对于本领域技术人员来说，上述详细披露仅仅作为示例，而并不构成对本申请的限定。虽然此处并没有明确说明，本领域技术人员可能会对本申请进行各种修改、改进和修正。该类修改、改进和修正在本申请中被建议，所以该类修改、改进、修正仍属于本申请示范实施例的精神和范围。
同时，本申请使用了特定词语来描述本申请的实施例。如“一个实施例”、“一实施例”、和/或“一些实施例”意指与本申请至少一个实施例相关的某一特征、结构或特点。因此，应强调并注意的是，本说明书中在不同位置两次或多次提及的“一实施例”或“一个实施例”或“一个替代性实施例”并不一定是指同一实施例。此外，本申请的一个或多个实施例中的某些特征、结构或特点可以进行适当的组合。
同理，应当注意的是，为了简化本申请披露的表述，从而帮助对一个或多个发明实施例的理解，前文对本申请实施例的描述中，有时会将多种特征归并至一个实施例、附图或对其的描述中。但是，这种披露方法并不意味着本申请对象所需要的特征比权利要求中提及的特征多。实际上，实施例的特征要少于上述披露的单个实施例的全部特征。
最后，应当理解的是，本申请中所述实施例仅用以说明本申请实施例的原则。其他的变形也可能属于本申请的范围。因此，作为示例而非限制，本申请实施例的替代配置可视为与本申请的教导一致。相应地，本申请的实施例不仅限于本申请明确介绍和描述的实施例。

Claims (21)

  1. 一种可穿戴设备，包括：
    至少两个电极，被配置为贴合人体皮肤以采集所述人体的心电信号；
    穿戴结构，被配置为承载所述至少两个电极，并将所述至少两个电极贴合在所述人体的正中矢状面的两侧；以及
    处理器，基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态。
  2. 根据权利要求1所述的可穿戴设备，所述身体状态至少包括放松状态和紧张状态，所述基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态包括：
    所述心率变异性不大于预设阈值时，所述人体运动时的身体状态为紧张状态；所述心率变异性大于预设阈值时，所述人体运动时的身体状态为放松状态。
  3. 根据权利要求1所述的可穿戴设备，所述基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态包括：
    基于所述心电信号确定所述人体的心率变异性和心率，根据所述心率变异性和所述心率确定所述人体的身体状态。
  4. 根据权利要求3所述的可穿戴设备，所述身体状态至少包括放松状态和紧张状态，所述根据所述心率变异性和所述心率确定所述人体的身体状态包括：
    所述心率变异性不大于预设阈值且所述心率大于预设心率阈值时，所述人体运动时的身体状态为紧张状态；
    所述心率变异性大于预设阈值且所述心率不大于所述预设心率阈值时，所述人体运动时的身体状态为放松状态。
  5. 根据权利要求1所述的可穿戴设备，所述身体状态至少包括放松状态和紧张状态，所述基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态包括：
    通过完成训练的机器学习模型，至少以所述心率变异性作为输入数据，所述机器学习模型基于所述输入数据输出所述人体的身体状态。
  6. 根据权利要求1所述的可穿戴设备，所述身体状态至少包括放松状态和紧张状态，所述基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态包括：
    获取标定曲线，所述标定曲线根据对所述人体处于不同身体状态下所产生的心率变异性拟合获得；
    根据所述标定曲线确定所述人体的身体状态。
  7. 根据权利要求1所述的可穿戴设备，其中，所述可穿戴设备还包括第一应变传感器，所述第一应变传感器位于所述穿戴结构上，并与所述人体的腰部区域相贴合，所述第一应变传感器基于所述人体呼吸时腰部区域的起伏变化采集所述人体的呼吸状态信息。
  8. 根据权利要求7所述的可穿戴设备，其中，所述基于所述心电信号确定所述人体的心率变异性，并至少根据所述心率变异性确定所述人体的身体状态包括：根据所述心率变异性和所述呼吸状态信息确定所述人体的身体状态。
  9. 根据权利要求8所述的可穿戴设备，所述身体状态至少包括放松状态和紧张状态，所述根据所述心率变异性和所述呼吸状态信息确定所述人体运动时的身体状态包括：
    所述心率变异性大于预设阈值且所述呼吸状态信息在预设呼吸状态信息范围内时，所述人体运动时的身体状态为放松状态；
    所述心率变异性不大于预设阈值且所述呼吸状态信息未在所述预设呼吸状态信息范围内时，所述人体运动时的身体状态为紧张状态。
  10. 根据权利要求8所述的可穿戴设备，所述身体状态至少包括放松状态和紧张状态，所述根据所述心率变异性和所述呼吸状态信息确定所述人体的身体状态包括：
    通过完成训练的机器学习模型，至少以所述心率变异性和所述呼吸状态信息作为输入数据，所述机器学习模型基于所述输入数据输出所述人体的身体状态。
  11. 根据权利要求1-10任一项所述的可穿戴设备，还包括反馈模块，所述反馈模块与所述处理器通信连接，所述处理器响应于所述人体的身体状态发出控制指令，所述反馈模块基于控制指令向用户发出反馈信息。
  12. 根据权利要求11所述的可穿戴设备，所述反馈模块包括扬声器，所述扬声器基于控制指令控制所述扬声器进入工作状态或切换音频信号。
  13. 根据权利要求12所述的可穿戴设备，所述扬声器进入状态或切换音频信号后，所述处理器继续至少基于所述心电信号判断所述人体的身体状态，若所述人体的身体状态仍处于紧张状态，所述处理器切换音频信号，直至所述人体的身体状态进入放松状态。
  14. 根据权利要求11所述的可穿戴设备，其中，所述人体的身体状态与所述反馈信息相关联。
  15. 根据权利要求1所述的可穿戴设备，其中，所述至少两个电极间隔分布于所述穿戴结构上，当所述人体佩戴所述穿戴结构时，所述至少两个电极位于所述人体腰部区域的正中矢状面的两侧。
  16. 根据权利要求15所述的可穿戴设备，其中，所述至少两个电极包括第一电极和第二电极，其中，当人体佩戴所述穿戴结构时，所述第一电极和所述第二电极分别贴合在所述人体的正中矢状面两侧的髂骨、后腰或腹部。
  17. 根据权利要求16所述的可穿戴设备，其中，当人体佩戴所述穿戴结构时，所述第一电极和所述第二电极关于所述人体的正中矢状面对称设置。
  18. 根据权利要求15所述的可穿戴设备，其中，所述至少两个电极包括第一电极，第二电极和参考电极，所述第一电极和第二电极间隔设置于所述穿戴结构靠近所述人体皮肤的表面，所述参考电极并位于所述第一电极和所述第二电极之间。
  19. 根据权利要求7所述的可穿戴设备，其中，所述穿戴结构为裤装，所述可穿戴设备还包括第二应变传感器，所述第二应变传感器位于所述穿戴结构与人体腿部或臀部对应的位置处；所述处理器进一步被配置为基于所述第二应变传感器识别所述人体下肢的运动动作。
  20. 根据权利要求19所述的可穿戴设备，其中，不同所述运动动作对应不同的所述心率变异性，所述处理器进一步被配置为基于所述心率变异性和所述运动动作评估所述运动动作。
  21. 根据权利要求19所述的可穿戴设备，所述可穿戴设备包括肌电模块，所述肌电模块用于采集所述人体的肌电信号，所述处理器基于所述肌电信号、所述运动动作以及呼吸状态信息、心率或所述心率变异性中的一种或几种评估所述人体的运动动作。
PCT/CN2023/075666 2022-11-17 2023-02-13 一种可穿戴设备 Ceased WO2024103547A1 (zh)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CN202380030543.5A CN118973475A (zh) 2022-11-17 2023-02-13 一种可穿戴设备
EP23889996.7A EP4509043A4 (en) 2022-11-17 2023-02-13 Wearable device
CN202321404344.XU CN220778334U (zh) 2022-11-17 2023-06-05 一种可穿戴设备
US18/936,984 US20250057461A1 (en) 2022-11-17 2024-11-04 Wearable device

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
PCT/CN2022/132671 WO2024103357A1 (zh) 2022-11-17 2022-11-17 一种可穿戴设备及心脏监控系统
CNPCT/CN2022/132671 2022-11-17

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/936,984 Continuation US20250057461A1 (en) 2022-11-17 2024-11-04 Wearable device

Publications (1)

Publication Number Publication Date
WO2024103547A1 true WO2024103547A1 (zh) 2024-05-23

Family

ID=91052570

Family Applications (2)

Application Number Title Priority Date Filing Date
PCT/CN2022/132671 Ceased WO2024103357A1 (zh) 2022-11-17 2022-11-17 一种可穿戴设备及心脏监控系统
PCT/CN2023/075666 Ceased WO2024103547A1 (zh) 2022-11-17 2023-02-13 一种可穿戴设备

Family Applications Before (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/132671 Ceased WO2024103357A1 (zh) 2022-11-17 2022-11-17 一种可穿戴设备及心脏监控系统

Country Status (4)

Country Link
US (1) US20250064373A1 (zh)
EP (1) EP4512320A4 (zh)
CN (2) CN119072269A (zh)
WO (2) WO2024103357A1 (zh)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102488501A (zh) * 2011-11-17 2012-06-13 深圳市科瑞康实业有限公司 身心放松训练的辅助装置及呼吸引导模型显示处理方法
CN204293140U (zh) * 2014-10-12 2015-04-29 吴健康 一种心率变异性生物反馈康复装置
CN105496377A (zh) * 2014-10-08 2016-04-20 吴健康 一种心率变异生物反馈锻炼系统方法和设备
CN108209902A (zh) * 2017-05-25 2018-06-29 深圳市未来健身衣科技有限公司 运动员竞技状态评估方法及系统
US20200035337A1 (en) * 2015-06-17 2020-01-30 Followflow Holding B.V. Method and product for determining a state value, a value representing the state of a subject
CN111527494A (zh) * 2017-09-05 2020-08-11 B安全有限公司 可穿戴的认证设备
CN115137299A (zh) * 2021-03-31 2022-10-04 华为技术有限公司 一种确定训练状态的方法及装置

Family Cites Families (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IT1255065B (it) * 1992-05-22 1995-10-17 Rotolo Giuseppe Dispositivo di posizionamento di elettrodi per elettrocardiografia
US20110028821A1 (en) * 2009-07-31 2011-02-03 Newcardio, Inc. Electrocardiographic Monitoring System and Method Using Orthogonal Electrode Pattern
CN201790810U (zh) * 2010-07-30 2011-04-13 江阴市协和针织有限公司 穿戴式心电采集装置
FR2970168B3 (fr) * 2011-01-10 2013-02-08 Chin-Yeh Hung Capteur de signal d'electrocardiogramme avec une ceinture
JP2014195514A (ja) * 2013-03-29 2014-10-16 ソニー株式会社 生体情報取得装置及び生体情報通信システム
CN103315722A (zh) * 2013-06-12 2013-09-25 浙江大学 穿戴式人体多生理参数采集装置
CN103908247B (zh) * 2014-04-08 2015-10-14 北京邮电大学 一种可穿戴式心电信号实时采集装置
US9662030B2 (en) * 2014-10-01 2017-05-30 Verily Life Sciences Llc Electrocardiography device for garments
CN204618226U (zh) * 2015-04-14 2015-09-09 祝兴亮 一种腰带式可穿戴无线心电监测仪
WO2017033058A1 (en) * 2015-08-24 2017-03-02 L.I.F.E. Corporation S.A. Physiological monitoring garments with enhanced sensor stabilization
CN106510689A (zh) * 2016-12-30 2017-03-22 东南大学 一种穿戴式多生理参数采集装置
CN207071088U (zh) * 2017-01-25 2018-03-06 杭州三目科技有限公司 一种基于服装的人体运动监测、分析和反馈装置
CN208114591U (zh) * 2017-05-03 2018-11-20 长春爱思钛克智能科技有限公司 一种可穿戴式心电、呼吸信号实时监测的智能运动内衣
US20180344171A1 (en) * 2017-06-06 2018-12-06 Myant Inc. Sensor band for multimodal sensing of biometric data
CN210447000U (zh) * 2019-03-29 2020-05-05 福州益得康信息科技有限公司 用于心电监护与预警系统的心电信号采集软板
CN210843039U (zh) * 2019-05-20 2020-06-26 山东师范大学 可穿戴电子服装的心电信号采集装置及监测系统
CN215305956U (zh) * 2021-04-12 2021-12-28 方琰 一种穿戴式心电实时监护装置

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102488501A (zh) * 2011-11-17 2012-06-13 深圳市科瑞康实业有限公司 身心放松训练的辅助装置及呼吸引导模型显示处理方法
CN105496377A (zh) * 2014-10-08 2016-04-20 吴健康 一种心率变异生物反馈锻炼系统方法和设备
CN204293140U (zh) * 2014-10-12 2015-04-29 吴健康 一种心率变异性生物反馈康复装置
US20200035337A1 (en) * 2015-06-17 2020-01-30 Followflow Holding B.V. Method and product for determining a state value, a value representing the state of a subject
CN108209902A (zh) * 2017-05-25 2018-06-29 深圳市未来健身衣科技有限公司 运动员竞技状态评估方法及系统
CN111527494A (zh) * 2017-09-05 2020-08-11 B安全有限公司 可穿戴的认证设备
CN115137299A (zh) * 2021-03-31 2022-10-04 华为技术有限公司 一种确定训练状态的方法及装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4509043A4 *

Also Published As

Publication number Publication date
WO2024103357A1 (zh) 2024-05-23
EP4512320A4 (en) 2025-08-13
US20250064373A1 (en) 2025-02-27
CN118044819A (zh) 2024-05-17
EP4512320A1 (en) 2025-02-26
CN119072269A (zh) 2024-12-03

Similar Documents

Publication Publication Date Title
US20230277800A1 (en) Sleep performance system and method of use
US20250057461A1 (en) Wearable device
US10376155B2 (en) Method and system for generating physiological signals with fabric capacitive sensors
US20180184735A1 (en) Physiological monitoring garments with enhanced sensor stabilization
CN115701278B (zh) 用于肌电信号采集和处理的设备和方法
WO2020190514A1 (en) Medical therapy arrangement for applying an electrical stimulation to a human or animal subject
CN107529995A (zh) 用于传送刺激的装置、系统以及方法
CA3184173A1 (en) Modular physiologic monitoring systems, kits, and methods
KR102714769B1 (ko) 전기적 근육 자극 시스템
JP6084361B2 (ja) 呼吸センサ
Yoon et al. Evaluation of gait-assistive soft wearable robot designs for wear comfort, focusing on electroencephalogram and satisfaction
US20230096206A1 (en) Method and Device for Diagnosing Anterior Cruciate Ligament Injury Susceptibility
KR20160076630A (ko) 유연성 생체신호 측정센서
US20250194955A1 (en) Methods and systems for processing motion data
WO2024103547A1 (zh) 一种可穿戴设备
CN118078295A (zh) 一种可穿戴设备及心脏监控系统
CN215135492U (zh) 一种具有心理检测功能的连体治疗衣
CN111921063B (zh) 一种性欲控制方法及其控制系统
US20250017509A1 (en) Electrocardio monitoring systems and methods
KR102903208B1 (ko) 통증자극 및 진동자극을 제공하는 피부 근전도 측정 장치 및 이를 이용한 시스템
Annapoorani et al. Analysis of EMG Parameters to Predict Fatigue during Exercise in Young Athletes
US20250241553A1 (en) Advanced wearable hydration monitoring system
Wua et al. The Ergonomic Evaluations of Three Front Baby Carriers
CN119302606A (zh) 一种心电监控系统
CN118253079A (zh) 一种运动数据处理方法及系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23889996

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 202380030543.5

Country of ref document: CN

WWE Wipo information: entry into national phase

Ref document number: 2023889996

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2023889996

Country of ref document: EP

Effective date: 20241112

NENP Non-entry into the national phase

Ref country code: DE