WO2018214523A1 - Procédé et appareil d'acquisition de signaux électromyographiques - Google Patents

Procédé et appareil d'acquisition de signaux électromyographiques Download PDF

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Publication number
WO2018214523A1
WO2018214523A1 PCT/CN2018/072324 CN2018072324W WO2018214523A1 WO 2018214523 A1 WO2018214523 A1 WO 2018214523A1 CN 2018072324 W CN2018072324 W CN 2018072324W WO 2018214523 A1 WO2018214523 A1 WO 2018214523A1
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acquisition
module
skin impedance
action type
data analysis
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PCT/CN2018/072324
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English (en)
Chinese (zh)
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包磊
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深圳市前海未来无限投资管理有限公司
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Publication of WO2018214523A1 publication Critical patent/WO2018214523A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—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

Definitions

  • the invention belongs to the technical field of wearable electronic devices, and in particular relates to a method and device for collecting myoelectric signals.
  • Electromyographic signals are a temporal and spatial superposition of motor unit action potentials (MUAP) in many muscle fibers.
  • SEMG surface electromyography
  • SEMG surface electromyography
  • SEMG is a comprehensive effect of superficial muscle EMG and nerve trunk electrical activity on the surface of the skin, which can reflect the activity of neuromuscular to some extent. It is an important organism for evaluating the motor function of the neuromuscular system. Information carriers have been widely used in medical research, clinical diagnosis and rehabilitation.
  • myoelectric signals have begun to be applied in the field of sports biomechanics. Specifically, in the process of performing exercise training by the user, the myoelectric signals of specific parts of the human body can be collected, and based on the analysis results of the myoelectric signals, the user is Analysis and guidance of exercise effects.
  • the acquisition electrode attached to the surface of the skin usually collects the SEMG according to a preset acquisition mode.
  • the active state of different muscles tends to be different. Therefore, relying on a single acquisition mode to obtain myoelectric signals reduces the effectiveness and accuracy of the collection of myoelectric signals.
  • the embodiments of the present invention provide a method and device for collecting myoelectric signals, so as to solve the problem of low availability and low accuracy of the collection of myoelectric signals in the prior art.
  • a first aspect of the embodiments of the present invention provides a method for collecting an electromyogram signal, including:
  • the acquisition module in the control wearable device acquires the myoelectric signal from the preset human body position at the acquisition frequency.
  • a second aspect of the embodiments of the present invention provides an EMG signal acquisition apparatus, including:
  • a first obtaining unit configured to acquire an action type of an action to be performed by the user
  • An input unit configured to input the action type into a data analysis model to determine an acquisition frequency that matches the action type
  • control unit configured to control the acquisition module in the wearable device to collect the myoelectric signal from the preset human body position at the acquisition frequency.
  • the embodiment of the present invention has the beneficial effects that since the action type of the user training action has a great correlation with the active state of the muscle, by knowing the action type of the action that the user should currently perform,
  • the acquisition module in the wearable device acquires the myoelectric signal of the human body position attached thereto by using an acquisition frequency corresponding to the type of action
  • the acquisition frequency of the wearable device can be correlated with the actual muscle active state at the current time.
  • the acquisition module in the wearable device no longer relies on a single acquisition mode to acquire the myoelectric signal, but specifically uses different acquisition frequencies with different muscle activity levels, thereby improving the collection of the myoelectric signal. Effectiveness and accuracy.
  • FIG. 1 is a flowchart of implementing an electromyography signal acquisition method according to an embodiment of the present invention
  • FIG. 2 is a specific implementation flowchart of an electromyography signal collection method S102 according to an embodiment of the present invention
  • FIG. 3 is a flowchart of a specific implementation of an electromyography signal collection method S202 according to an embodiment of the present invention
  • FIG. 4 is a flowchart of an implementation of a method for collecting an electromyogram signal according to another embodiment of the present invention.
  • FIG. 5 is a flowchart of implementing an electromyography signal collection method according to another embodiment of the present invention.
  • FIG. 6 is a structural block diagram of an electromyogram signal collecting apparatus according to an embodiment of the present invention.
  • the wearable device may be a wearable smart fitness garment, or may be a collection of one or more collection modules that are wearable and attachable.
  • the wearable device when the wearable device is a wearable smart fitness garment, it may be a garment or pants made of a flexible fabric, and a plurality of collection modules are embedded on the side of the flexible fabric close to the human skin. Each collection module is fixed at different points of the smart fitness garment so that after the user wears the smart fitness garment, each collection module can be attached to each muscle of the user's body.
  • at least one control module is also embedded, and each of the acquisition modules is separately connected to the control module.
  • a wire and a circuit board may be disposed in the wearable device, wherein the circuit board is used to fix various communication buses and the acquisition module.
  • the circuit board and its various solder joints are wrapped with a waterproof glue.
  • the wearable device can be washed by fixing a waterproof trace on the laundry.
  • each acquisition module may include only an acquisition electrode having a somatosensory sensor function, or an integrated circuit having an acquisition function.
  • the above collection electrodes include, but are not limited to, fabric electrodes, rubber electrodes, gel electrodes, and the like.
  • each acquisition module is an integrated circuit having an acquisition function and a wireless transmission function, and the integrated circuit includes the above-mentioned acquisition electrode having a somatosensory sensor function.
  • the EMG signal collected by the acquisition module is transmitted to the remote control module through the wireless network, and the control module is located in the remote terminal device or the remote control box used in conjunction with the acquisition module.
  • FIG. 1 is a flowchart showing an implementation process of an electromyogram signal collection method according to an embodiment of the present invention. As shown in FIG. 1 , the method includes steps S101 to S103, which are described in detail as follows:
  • S101 Acquire an action type of an action that the user is about to perform.
  • the application client supporting the wearable device stores video data of multiple sets of fitness exercise programs, and the video data can be displayed in the display interface of the application client.
  • the user runs the application client on the terminal device and selects a set of exercise programs that he needs.
  • the terminal device displays the video data corresponding to the exercise plan, and the user can view the exercise instruction action that should be performed at each moment in the display screen.
  • the user will simulate the exercise guidance action played in the video to perform the limb movement. Therefore, the action corresponding to each moment in the video data is the same as the action to be performed by the user at that moment.
  • the action type of the action to be performed by the user is determined by acquiring the action type of the action corresponding to each moment in the video data.
  • the motion event is triggered when the terminal device receives the exercise plan selection instruction issued by the user. From this point on, the counter inside the terminal device starts to operate for cumulative calculation of the number of pulses. The more the cumulative pulse number corresponding to a certain moment, the longer the video playback duration corresponding to the moment.
  • the terminal device can acquire storage parameters corresponding to different locations in the video data.
  • the above storage parameters include the type of action and the duration of the action type.
  • the action type can be used to identify a unique action.
  • the terminal device After the motion event is triggered, when the number of accumulated pulses of the counter reaches 5, the terminal device will acquire the action type of the time when the number of pulses in the video data specified by the user is 5, and the duration of the push-up is 3 minute.
  • the action type is transmitted to the control module.
  • S102 Input the action type into a data analysis model to determine an acquisition frequency that matches the action type.
  • the data analysis model is an output program preset in the control module.
  • the data analysis model After the action type is acquired at the current time, the data analysis model starts to analyze and process the action type, and automatically recognizes the motion characteristics of the action type, thereby calculating and outputting after determining the action composition of the action type and the frequency of the action change.
  • An EMG signal acquisition frequency that is adapted to the composition of the motion and the frequency of the motion change, and thus the control module can determine an EMG signal acquisition frequency that is most suitable for the type of motion.
  • the action composition of the push-ups includes two actions of lowering the body and urging the body to return the body to the starting position.
  • the data analysis model is pre-set with two switching frequencies based on the two actions, so the data analysis model will output the EMG signal acquisition frequency associated with the two switching frequencies.
  • S103 Control an acquisition module in the wearable device to acquire a myoelectric signal from the preset human body position at the acquisition frequency.
  • the control module controls the respective acquisition modules on the wearable fitness garment to collect the myoelectric signals from the preset human body position according to the acquisition frequency of the data analysis model output. Specifically, when the acquisition module is in communication with the control module and the acquisition module only includes the collection electrode, the control module outputs a high level signal to turn on the connection between each collection module and the control module, thereby realizing the collection of the myoelectric signal. Control; when the acquisition module and the control module are connected by wireless, the control module sends a control data packet to the acquisition module, so that the acquisition module connected to the control data packet can perform the collection of the myoelectric signal according to the control parameter in the control data packet. .
  • the preset human body position refers to the position of the human body that the collection module is in contact with.
  • the position of the human body contacted by different acquisition modules is different, that is, the corresponding muscle parts are different. Therefore, the control module can collect the electromyography from different muscle parts through the collection module.
  • the signal is transmitted to the terminal device for subsequent analysis processing.
  • the acquisition module in the wearable device since the action type of the user training action has a great correlation with the active state of the muscle, the acquisition module in the wearable device utilizes the corresponding action by knowing the action type of the action that the user should currently perform.
  • the acquisition frequency of the wearable device can be correlated with the actual muscle active state at the current time, so that the acquisition module in the wearable device is no longer.
  • the foregoing S102 specifically includes:
  • S201 Determine, in the N collection modules of the wearable device, M acquisition modules that match the action type.
  • the total number of collection modules on the wearable device is N, and N is an integer greater than 1.
  • N is an integer greater than 1.
  • the control module After obtaining the action type, the control module starts to comprehensively analyze the action type, and automatically recognizes the action component in the action type and the active muscle block corresponding to each component action, which is currently pre-stored in the control module.
  • the collection module attaches the position correspondence table, and matches the attachment positions to the respective collection modules of the above-mentioned active muscle blocks, thereby determining each of the above collection modules as an acquisition module that needs to perform the electromyography signal collection work.
  • the action type is push-ups
  • you can know that its action consists of lowering the body and urging the body.
  • the muscle blocks in the active exercise state are the triceps and deltoid anterior bundles; when the body is vigorously propped up, the muscle blocks in the active exercise state are the anterior serratus and the diaphragm
  • the control module In the collection module attachment position correspondence table find the respective collection modules attached to the triceps, deltoid toe, anterior serratus, and diaphragm, respectively, assuming that these acquisition modules are A, B, C, and D, respectively. Then, it is determined that the acquisition modules that need to perform the electromyography signal acquisition work are A, B, C, and D.
  • S202 Input the action type into a data analysis model corresponding to each of the M acquisition modules, to respectively determine acquisition frequencies that match the M acquisition modules and the action type.
  • a plurality of data analysis models are pre-configured in the control module, and each data analysis module corresponds to an acquisition module on the wearable device.
  • Each data analysis model is only used to output the EMG signal acquisition frequency associated with the acquisition module. Since the position of the human body to which the acquisition module is attached is known, it is possible to determine whether the muscle mass of the human body position is in a state of being exerted or in a relaxed state in the case where the type of action is also known. In an action type, based on the pre-measured force time point of the muscle block, the data analysis model will output an acquisition frequency that matches the force time point. For a certain acquisition module, when the muscle attached to the acquisition module is in a state of force, the acquisition frequency calculated by the data analysis model is high; when the muscle attached to the acquisition module is in a relaxed state The acquisition frequency calculated by the data analysis model is low.
  • the action type is a sit-up exercise
  • the action corresponding to the time T 1 is "sleeping" and the action corresponding to the time T 2 is "starting”
  • the action of the action type at time T 1 is known.
  • the muscle mass, the abdominal muscles will be in a relaxed state, and at the time T 2 the active muscle mass of the action type will be in a state of exertion. Therefore, according to the two switching durations corresponding to the two component actions, the data analysis model corresponding to the acquisition module attached to the abdominal muscle is processed, and the acquisition frequency corresponding to the switching duration is output, so that the time T 1 is The corresponding acquisition frequency is lower than the acquisition frequency corresponding to the time T 2 .
  • the EMG signal When the muscle is in the state of force, the EMG signal is acquired based on the higher acquisition frequency, and the amount of data collected per unit time will be more. Therefore, in the process of analyzing the EMG signal by the subsequent application client, Based on the EMG signal with higher accuracy of data acquisition under the force state, it can make a more accurate evaluation of the exercise effect of the muscle.
  • the EMG signal When the muscle is in a relaxed state, the EMG signal is acquired based on the lower acquisition frequency, and the amount of data collected per unit time will be less. Since the EMG signal collected in the relaxed state has a low evaluation effect on the muscle exercise effect, the EMG signal with less data can improve the analysis efficiency of the application client.
  • the control module For each acquisition module on the wearable device, after obtaining the acquisition frequency output by the data analysis model corresponding to the acquisition module through the above S202, if the acquisition module only includes the collection electrode, the control module will use the acquisition frequency as the access frequency.
  • the wire connected to the acquisition module is controlled to be connected to the circuit, so that the collection electrode in the acquisition module can collect the myoelectric signal when the circuit is connected.
  • the control module directly sends the control data packet carrying the acquisition frequency to the acquisition module, so that the acquisition module can realize the collection of the myoelectric signal according to the acquisition frequency.
  • the control module can separately control one acquisition module to collect the EMG signal.
  • the acquired data is the myoelectric signal generated by the muscles under motion, thereby improving the collection efficiency of the myoelectric signal.
  • FIG. 3 shows a specific implementation process of the electromyography signal collection method S202 provided by the embodiment of the present invention, which is described in detail as follows:
  • S301 Determine, for a human body position attached to each of the M acquisition modules, a muscle fatigue index of the human body position based on a historical electromyogram signal collected from the human body position.
  • the collected myoelectric signal will be wirelessly transmitted to the application client of the terminal device.
  • the application client stores the EMG signals received each time according to its corresponding acquisition module. That is, all myoelectric signals collected by the same acquisition module are stored in the same record.
  • the application client reads the historical EMG signals stored in the same record. Before the reading, if it is judged that the data amount of the currently stored historical electromyogram signal has exceeded the preset threshold, only the historical myoelectric signal stored in the most recent preset time period is read. Thereafter, the read historical EMG signal is analyzed and processed using a preset algorithm to determine the muscle fatigue index. Since these historical EMG signals are derived from the same acquisition module, the determined muscle fatigue index is also only the muscle fatigue index of the muscle mass of the human body position to which the acquisition module is attached.
  • the preset algorithm for determining the muscle fatigue index includes, but is not limited to, a myoelectric signal linear analysis technique, an electromyographic signal frequency analysis technique, and a complex covariance function fatigue estimation method.
  • S302 Input the muscle fatigue index and the action type into a data analysis model corresponding to the collection module to determine an acquisition frequency of the acquisition module.
  • the application client transmits the calculated muscle fatigue index corresponding to each acquisition module to the control module.
  • the control module inputs the muscle fatigue index and the action type of the action that the user is about to perform at the current time to the data analysis model corresponding to the acquisition module, when the muscle fatigue index corresponding to the acquisition module is received.
  • the muscle fatigue index and the action type are comprehensively analyzed, and the acquisition frequency of the acquisition module is calculated.
  • the lower the muscle fatigue index and the more active the muscle the lower the possibility that the current user's muscle can sustain a large movement, and the activity of the muscle per unit time is relatively more than that of other muscles. Low, therefore, the calculated frequency of EMG signal acquisition will be lower than the initial value of the acquisition frequency corresponding to the acquisition module in the initial state.
  • the acquisition frequency is determined based on the fatigue index of the muscle attached by the collection module and the action type of the action to be performed by the user, so that the collection frequency used by the wearable device on the acquisition module can be more consistent.
  • the real-time active condition of the muscle ensures that the acquisition frequency of the data analysis model output can be more accurate, and the EMG signal is collected based on the precise acquisition frequency, so that the collected data also has higher reference value and improves the collection of the EMG signal. Effectiveness.
  • the method further includes:
  • S401 For each of the collection modules, obtain a first skin impedance of a human body position attached to the collection module in the wearable device.
  • a loop may be formed between the control module and the acquisition module in the wearable device and the human body to which the acquisition module is attached.
  • the total impedance of the human skin, blood, muscle, cell tissue, and the junction thereof including the electric resistance and the capacitance is the first skin impedance described above.
  • the acquisition module will be attached to the skin surface of the user.
  • the human skin between any two acquisition electrodes is equivalent to a human body resistance, and then multiple human skins are equivalent to a plurality of series resistors in the loop.
  • a preset resistor with a known resistance is connected in series inside the control module. The voltage between any two acquisition electrodes in the acquisition module and the voltage on the preset resistor can be measured by the control module. Since the user's body is not the same, the voltage between the collecting electrodes changes as the contact pressure of the collecting electrode and the contact area change.
  • the magnitude of each body resistance can be calculated based on the known resistance of the preset resistor. Therefore, by this measurement method, the first skin impedance of the human body position to which each acquisition module is attached can be obtained.
  • S402 Adjust a model parameter in a data analysis model corresponding to the collection module according to a difference between the first skin impedance and a preset second skin impedance.
  • Each collection module includes two collection electrodes, and the skin impedance between the two collection electrodes has a preset standard value measured at the factory, and the preset standard value is the preset second skin impedance.
  • the data analysis model corresponding to the acquisition module is a mathematical model established according to the preset second skin impedance. Therefore, in order to make the acquisition frequency calculated by the data analysis model more closely match the user's personal characteristics, the measurement is obtained according to S401. After the first skin impedance corresponding to the acquisition module, the first skin impedance is compared with the second skin impedance, and the model parameters of the data analysis model are proportionally based on the magnitude of the error between the factory impedance value and the actual impedance value. The calibration process is performed to ensure that the data analysis model outputs a more accurate acquisition frequency during the user's motion, so that the wearable device can achieve a more effective acquisition frequency adjustment effect.
  • the method further includes:
  • the skin impedance corresponding to the acquisition module at the current time is measured based on the amplitude of the myoelectric signal.
  • S502 Determine whether a difference between the third skin impedance and the first skin impedance exceeds a preset threshold.
  • the preset threshold is preset in the control module when the wearable device is shipped from the factory, and can also be sent by the application client to the control module after the user performs custom adjustment in the application client.
  • the data analysis model has become a data analysis model conforming to the user's personal characteristics after being calibrated, and thus the standard skin impedance in each data analysis model is the first skin impedance measured in the non-moving state.
  • the user is often accompanied by sweating during exercise.
  • the skin impedance of the human body is not a fixed value. If the skin is wet, sweaty, damaged, or contaminated with conductive dust on the skin surface, the skin impedance will be reduced.
  • the degree of deviation of the current skin impedance from the standard skin impedance can be known. .
  • the parameters of the data analysis model can be re-adjusted under the condition that the difference in skin impedance variation is too large, so that the data analysis is performed every moment.
  • the models can be roughly matched with the objective situation of the user, which improves the adaptability of the data analysis model and makes the calculated acquisition frequency more accurate.
  • the model calibration work is performed only when the skin impedance change difference exceeds the threshold value, thereby avoiding the control module to perform model parameter adjustment processing every moment, and reducing the calculation pressure of the control module.
  • FIG. 6 is a structural block diagram of the EMG signal acquisition apparatus provided by the embodiment of the present invention. For the convenience of description, only the embodiment related to the embodiment of the present invention is shown. section.
  • the EMG signal acquisition device includes:
  • the first obtaining unit 61 is configured to acquire an action type of an action that the user is about to perform.
  • the determining unit 62 is configured to input the action type into a data analysis model to determine an acquisition frequency that matches the action type.
  • the control unit 63 is configured to control the acquisition module in the wearable device to collect the myoelectric signal from the preset human body position at the acquisition frequency.
  • the determining unit 62 includes:
  • the first determining subunit is configured to determine, in the N collection modules of the wearable device, M acquisition modules that match the action type.
  • a second determining subunit configured to input the action type into a data analysis model respectively corresponding to the M collection modules, to respectively determine an acquisition frequency of the M acquisition modules that match the action type.
  • the second determining subunit is specifically configured to:
  • the muscle fatigue index of the human body position is determined based on the historical electromyogram signal collected from the human body position.
  • the muscle fatigue index and the action type are input into a data analysis model corresponding to the acquisition module to determine an acquisition frequency of the acquisition module.
  • the EMG signal acquisition device further includes:
  • a second acquiring unit configured to acquire, for each of the collecting modules, a first skin impedance of a human body position attached to the collecting module in the wearable device.
  • an adjusting unit configured to adjust a model parameter in the data analysis model corresponding to the collection module according to a difference between the first skin impedance and the preset second skin impedance.
  • control unit 63 includes:
  • the determining subunit is configured to determine whether a difference between the third skin impedance and the first skin impedance exceeds a preset threshold.
  • the adjusting subunit is configured to re-adjust the model parameters in the data analysis model corresponding to the collection module when the difference between the third skin impedance and the first skin impedance exceeds a preset threshold.
  • each functional unit and module described above is exemplified. In practical applications, the above functions may be assigned to different functional units as needed.
  • the module is completed by dividing the internal structure of the device into different functional units or modules to perform all or part of the functions described above.
  • Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application.
  • For the specific working process of the unit and the module in the foregoing system reference may be made to the corresponding process in the foregoing method embodiment, and details are not described herein again.
  • the disclosed apparatus and method may be implemented in other manners.
  • the system embodiment described above is merely illustrative.
  • the division of the module or unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be used. Combinations can be integrated into another system, or some features can be ignored or not executed.
  • the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be electrical, mechanical or otherwise.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the medium includes a plurality of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the methods described in various embodiments of the embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

La présente invention est applicable au domaine technique des dispositifs électroniques portables, et concerne un procédé et un appareil d'acquisition de signaux électromyographiques. Le procédé consiste : à obtenir le type de mouvement d'un mouvement à effectuer par un utilisateur ; à entrer le type de mouvement dans un modèle d'analyse de données pour déterminer une fréquence d'acquisition correspondant au type de mouvement ; et à commander un module d'acquisition dans un appareil pouvant être porté pour acquérir des signaux électromyographiques à partir de positions corporelles prédéfinies à ladite fréquence d'acquisition. Par apprentissage concernant le type de mouvement d'un mouvement que l'utilisateur doit effectuer actuellement, le procédé permet de déterminer une fréquence d'acquisition de signaux électromyographiques satisfaisant aux conditions d'exercice réelles actuelles, et permet ainsi d'associer la fréquence d'acquisition de l'appareil pouvant être porté à l'état d'activité musculaire de l'utilisateur au moment actuel, de sorte que le module d'acquisition dans l'appareil pouvant être porté ne repose plus sur un mode d'acquisition unique pour obtenir des signaux électromyographiques, mais utilise différentes fréquences d'acquisition d'une manière ciblée en fonction des différents états d'activité des muscles. Par conséquent, l'efficacité et la précision de l'acquisition des signaux électromyographiques sont améliorées.
PCT/CN2018/072324 2017-05-25 2018-01-12 Procédé et appareil d'acquisition de signaux électromyographiques WO2018214523A1 (fr)

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