CN104799854B - A kind of surface myoelectric harvester and its electromyographic signal processing method - Google Patents
A kind of surface myoelectric harvester and its electromyographic signal processing method Download PDFInfo
- Publication number
- CN104799854B CN104799854B CN201510211190.6A CN201510211190A CN104799854B CN 104799854 B CN104799854 B CN 104799854B CN 201510211190 A CN201510211190 A CN 201510211190A CN 104799854 B CN104799854 B CN 104799854B
- Authority
- CN
- China
- Prior art keywords
- operational amplifier
- resistance
- electrode
- module
- circuit
- 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.)
- Active
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
- A61B5/389—Electromyography [EMG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- 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
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Abstract
The invention discloses a kind of surface myoelectric harvester and its electromyographic signal processing method, including：First electrode, second electrode, the 3rd electrode, front end amplification module, leg drive module, bandpass filtering modules block, power frequency filtration module, level lifting module, AD acquisition modules, microprocessor and digital isolation module；The first electrode, second electrode connect bandpass filtering modules block by front end amplification module；3rd electrode connects front end amplification module by leg drive module；The bandpass filtering modules block, power frequency filtration module, level lifting module, AD acquisition modules, digital isolation module and microprocessor are sequentially connected；Human body reference electrical signal is extracted by leg drive module and serves repid discharge, the effect of effective attenuation human body common mode voltage signal, by front end amplification module, common-mode rejection ratio and input impedance is improved so that causes the electromyographic signal data precision collected high.
Description
Technical field
The present invention relates to the collection of skin surface electromyographic signal and processing technology field, more particularly to a kind of surface flesh
Electric harvester and its electromyographic signal processing method.
Background technology
Surface electromyogram signal (surface electromyography, SEMG) telecommunications adjoint when being contraction of muscle
Number, it is the important method in body surface Non-invasive detection muscle activity.Surface electromyogram signal is the motion raised by muscle excitation time
The sequence of action potential one by one (Motor Unit Action Potential Trains, MUAPT) that unit is produced is in skin
Surface is formed by stacking, and is a kind of small-signal of non-stationary.By extracting and studying surface electromyogram signal, people can be effectively recognized
Body athletic performance, diagnose disorder of muscle and instruct rehabilitation medical etc., be widely used in medical diagnosis on disease, medical science of recovery therapy, movable body
The field such as educate.
Existing myoelectric signal collection apparatus typically obtains skin surface electromyographic signal by electrode contact human body skin,
Electromyographic signal is handled by corresponding circuit again.But the electromyographic signal that existing myoelectric signal collection apparatus is collected
Common-mode rejection ratio is low, and input impedance is low, causes that the electromyographic signal data accuracy collected is low, and precision is low.
Therefore, prior art has yet to be improved and developed.
The content of the invention
The technical problem to be solved in the present invention is that there is provided a kind of surface myoelectric harvester and its electromyographic signal processing side
Method, it is intended to which the electromyographic signal for solving existing myoelectric signal collection apparatus collection is inaccurate, the problem of precision is low.
The technical proposal for solving the technical problem of the invention is as follows：
A kind of surface myoelectric harvester, wherein, including：
First electrode, second electrode and the 3rd electrode for obtaining skin surface electromyographic signal；
For the front end amplification module that myoelectricity differential signal is connected, extracted with first electrode and second electrode and is amplified；
Leg drive module for human body reference electrical signal to be connected, extracted with the 3rd electrode；
Bandpass filtering modules block for filtering out the signal beyond myoelectricity scope；
Power frequency filtration module for filtering out Hz noise；
Level lifting module for raising level；
For carrying out AD samplings to electromyographic signal, analog-to-digital conversion is the AD acquisition modules of corresponding data signal；
For the microprocessor handled data signal；
The digital isolation module interfered for reducing between AD acquisition modules and microprocessor；
The first electrode, second electrode connect bandpass filtering modules block by front end amplification module；3rd electrode leads to
Cross leg drive module connection front end amplification module；The bandpass filtering modules block, power frequency filtration module, level lifting module, AD
Acquisition module, digital isolation module and microprocessor are sequentially connected.
Described surface myoelectric harvester, wherein, the front end amplification module includes：Low-pass filter circuit, impedance
With circuit, high-pass filtering circuit, conducting wire shield guard circuit and bridge balance differential amplifier circuit；
The impedance matching circuit connects first electrode and second electrode respectively by low-pass filter circuit；The electric bridge is put down
The differential amplifier circuit that weighs connects the 3rd electrode by leg drive module；The impedance matching circuit is connected by high-pass filtering circuit
Connect bridge balance differential amplifier circuit；The conducting wire shield guard circuit connects the high-pass filtering circuit；The electric bridge is put down
Weigh differential amplifier circuit connection bandpass filtering modules block.
Described surface myoelectric harvester, wherein, the impedance matching circuit includes：First operational amplifier, second
Operational amplifier, the 4th resistance, the 5th resistance and the 6th resistance；
The low-pass filter circuit includes：First resistor, second resistance and the first electric capacity；
The in-phase input end of first operational amplifier connects first electrode by first resistor, and first computing is put
The in-phase input end of big device also passes through the in-phase input end of first the second operational amplifier of capacitance connection；First operation amplifier
The output end of device connects the inverting input of first operational amplifier by the 5th resistance；First operational amplifier
Inverting input by the 4th resistance connect the second operational amplifier inverting input, first operational amplifier it is anti-phase
Input also passes sequentially through the 4th resistance and the 6th resistance connects the output end of the second operational amplifier；First operation amplifier
The output end connection high-pass filtering circuit of device；The in-phase input end of second operational amplifier connects second by second resistance
Electrode；The output end connection high-pass filtering circuit of second operational amplifier.
Described surface myoelectric harvester, wherein, the high-pass filtering circuit includes the 9th resistance, the tenth resistance, the
Four electric capacity and the 5th electric capacity；
The bridge balance differential amplifier circuit includes：12nd resistance, the 13rd resistance and the 6th operational amplifier；
The in-phase input end of 6th operational amplifier passes through the 4th capacitance connection impedance matching circuit；6th fortune
The in-phase input end for calculating amplifier also passes sequentially through the anti-phase input of the 9th resistance and the tenth resistance the 6th operational amplifier of connection
End；The in-phase input end of 6th operational amplifier also passes sequentially through the 12nd resistance and the 13rd resistance connects the 6th computing
The inverting input of amplifier；The inverting input of 6th operational amplifier passes through the 5th capacitance connection impedance matching electricity
Road；The output end connection bandpass filtering modules block of 6th operational amplifier.
Described surface myoelectric harvester, wherein, the conducting wire shield guard circuit includes the 3rd operational amplifier；
The in-phase input end connection high-pass filtering circuit of 3rd operational amplifier；The inverting input of 3rd operational amplifier
Connect the output end of the 3rd operational amplifier；The output head grounding of 3rd operational amplifier.
Described surface myoelectric harvester, wherein, the leg drive module includes：3rd resistor, the 7th resistance,
Eight resistance, the 11st resistance, the 6th electric capacity, four-operational amplifier and the 5th operational amplifier；
The output end of 5th operational amplifier connects the 3rd electrode by 3rd resistor；5th operational amplifier
Output end also pass through the 7th resistance connect the 5th operational amplifier inverting input, the output of the 5th operational amplifier
End also passes sequentially through the 6th electric capacity and the 8th resistance connects the inverting input of the 5th operational amplifier；5th operation amplifier
The in-phase input end ground connection of device；
The output end of the four-operational amplifier connects the anti-phase input of the 5th operational amplifier by the 11st resistance
End；The output end of the four-operational amplifier is also connected with the inverting input of four-operational amplifier；4th computing is put
The in-phase input end connection front end amplification module of big device.
Described surface myoelectric harvester, wherein, the microprocessor includes：
Pretreatment unit, for being pre-processed to the corresponding data signal of the surface electromyogram signal, obtains corresponding
Myoelectricity data；
Muscular strength assessment unit, for dividing selected part myoelectricity number from the myoelectricity data using end-point detection algorithm
According to according to the corresponding root mean square of part myoelectricity data calculating and myoelectric integral value, according to the root mean square and myoelectric integral value
Muscle tensility is carried out to assess and the assessment of muscular strength grade；
Muscular fatigue assessment unit, for dividing selected part myoelectricity from the myoelectricity data using end-point detection algorithm
Data, corresponding average frequency and median frequency are calculated according to the part myoelectricity data, according to the average frequency and intermediate value frequency
Rate carries out muscular fatigue assessment.
Described surface myoelectric harvester, wherein, the muscular fatigue assessment unit includes：
Myoelectricity data extract subelement, for dividing selected part flesh from the myoelectricity data using end-point detection algorithm
Electric data；
Muscular fatigue computation subunit, the electromyographic signal for calculating the part myoelectricity data using short time discrete Fourier transform
Frequency spectrum, the degree of fatigue of muscle is obtained by the median frequency calculated.
A kind of electromyographic signal processing method using above-mentioned surface myoelectric harvester, wherein, including：
S1, collection skin surface electromyographic signal, and be converted to corresponding data signal；
S2, the data signal is pre-processed, obtain corresponding myoelectricity data；
S3, using end-point detection algorithm selected part myoelectricity data are divided from the myoelectricity data, according to the part flesh
Electric data calculate corresponding root mean square, myoelectric integral value, average frequency and median frequency；
S4, Muscle tensility assessment is carried out according to the root mean square and myoelectric integral value and muscular strength grade is assessed, according to described flat
Equal frequency and median frequency carry out muscular fatigue assessment.
A kind of surface myoelectric harvester provided by the present invention and its electromyographic signal processing method, are efficiently solved existing
The electromyographic signal of some myoelectric signal collection apparatus collections is inaccurate, the problem of precision is low, including：First electrode, second electrode,
3rd electrode, front end amplification module, leg drive module, bandpass filtering modules block, power frequency filtration module, level lifting module, AD
Acquisition module, microprocessor and digital isolation module；The first electrode, second electrode connect band logical by front end amplification module
Filtration module；3rd electrode connects front end amplification module by leg drive module；The bandpass filtering modules block, power frequency filter
Ripple module, level lifting module, AD acquisition modules, digital isolation module and microprocessor are sequentially connected；Mould is driven by leg
Block extracts human body reference electrical signal and serves repid discharge, the effect of effective attenuation human body common mode voltage signal, is put by front end
Big module, improves common-mode rejection ratio and input impedance so that cause the electromyographic signal data accuracy collected high, precision
Height, brings and greatly facilitates.
Brief description of the drawings
The structured flowchart for the surface myoelectric harvester preferred embodiment that Fig. 1 provides for the present invention；
Front end amplification module and leg driving mould in the surface myoelectric harvester Application Example that Fig. 2 provides for the present invention
The circuit diagram of block；
The method stream of the electromyographic signal processing method preferred embodiment for the surface myoelectric harvester that Fig. 3 provides for the present invention
Cheng Tu.
Embodiment
The present invention provides a kind of primary surface myoelectricity acquisition device and its electromyographic signal processing method, to make the mesh of the present invention
, technical scheme and advantage it is clearer, clear and definite, the present invention is described in more detail for the embodiment that develops simultaneously referring to the drawings.
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
Referring to Fig. 1, the structured flowchart for the surface myoelectric harvester preferred embodiment that Fig. 1 provides for the present invention, such as schemes
Shown, the surface myoelectric harvester includes：First electrode, second electrode and for obtaining skin surface electromyographic signal
Three electrodes；For the front end amplification module 110 that myoelectricity differential signal is connected, extracted with first electrode and second electrode and is amplified；
Leg drive module 120 for human body reference electrical signal to be connected, extracted with the 3rd electrode；For filtering out beyond myoelectricity scope
The bandpass filtering modules block 130 of signal；Power frequency filtration module 140 for filtering out Hz noise；Level for raising level is lifted
Rising mould block 150；For carrying out AD samplings to electromyographic signal, analog-to-digital conversion is the AD acquisition modules 160 of corresponding data signal；With
In the microprocessor 180 handled data signal；For reducing between AD acquisition modules 160 and microprocessor 180 mutually
The digital isolation module 170 of interference；The first electrode, second electrode connect bandpass filtering mould by front end amplification module 110
Block 130；3rd electrode connects front end amplification module 110 by leg drive module 120；The bandpass filtering modules block 130,
Power frequency filtration module 140, level lifting module 150, AD acquisition modules 160, digital isolation module 170 and microprocessor 180 according to
Secondary connection.
Specifically, the front end amplification module 110 obtains the myoelectricity of skin surface by first electrode and second electrode
Signal.The front end amplification module 110 is directly connected by multiple electrodes with human body, gathers the electromyographic signal of human body, and can be from
Extract and useful differential signal and then amplified under high common mode environment, the characteristics of with high input impedance, high cmrr.
The electromyographic signal of collection is sent into front end amplification module 110, the front end amplification module by first electrode and second electrode
110 can extract useful myoelectricity differential signal and then be amplified under high common mode environment, and can reach higher common mode inhibition
Than and high impedance, and myoelectricity differential signal is sent to bandpass filtering modules block 130.Front end amplification module 110 drives mould with leg
Block 120 coordinates to gather the front end electromyographic signal of high to-noise ratio.The leg drive module 120 extracts the reference electrical signal of human body
Interference for reducing circuit, connects the right leg or left leg of human body to gather electromyographic signal, in actual use by the 3rd electrode
When, typically connect right leg.The incoming bandpass filtering modules block 130 of output of front end amplification module 110 is so as to filter out beyond myoelectricity scope
Frequency electromyographic signal.Power frequency filtration module 140 is in series by two new integrated frequency filters, and the chip heat is made an uproar
Sound is small, and single stage filtering depth is deep.The electromyographic signal exported by power frequency filtration module 140 is lifted to through over level lifting module 150
More than baseline, then the progress AD samplings of process AD acquisition modules 160 are changed into data signal and recognized by microprocessor 180.Microprocessor
Device 180 and AD acquisition modules 160 add digital isolation module 170, to reduce interfering between two modules.The band logical
Filtration module 130, power frequency filtration module 140, level lifting module 150, AD acquisition modules 160, digital isolation module 170 are existing
Having in technology has a variety of implementations, and microprocessor can be using single-chip microcomputer serial STM32.
Referring to Fig. 2, Fig. 2 for front end amplification module in the surface myoelectric harvester Application Example that provides of the present invention and
The circuit diagram of leg drive module, further, the front end amplification module 110 include：Low-pass filter circuit 111, resistance
Anti- match circuit 112, high-pass filtering circuit 113, bridge balance differential amplifier circuit 114 and conducting wire shield guard circuit 115.
The front end amplification module 110 is made up of impedance matching circuit 112 and bridge balance differential amplifier circuit 114, front end amplification
Module 110 coordinates to gather the front end electromyographic signal of high to-noise ratio with leg drive module 120.
The impedance matching circuit 112 connects first electrode and second electrode respectively by low-pass filter circuit 111；It is described
Bridge balance differential amplifier circuit 114 connects the 3rd electrode by leg drive module 120；The impedance matching circuit 112 leads to
Cross the connection bridge balance of high-pass filtering circuit 113 differential amplifier circuit 114；The conducting wire shield guard circuit 150 connects institute
State high-pass filtering circuit 113；The bridge balance differential amplifier circuit 114 connects bandpass filtering modules block 130.
The existing myoelectricity Acquisition Circuit being connected with electrode, can not both meet requirement of the myoelectricity Acquisition Circuit to input impedance
（Its input impedance of Standard is more than 100M）, it more difficult to higher difference mode gain requirement is met, it is difficult to which meeting bridge balance makes
Obtaining common-mode rejection ratio tends to be infinitely great.And present invention employs myoelectricity impedance matching circuit, driven-right-leg circuit and with balance
The differential amplifier circuit of electric bridge, improves input impedance and common-mode rejection ratio etc., and difference channel is improved to the mould of bridge balance
Formula, further with the addition of symmetrical none-inverting input circuits so that common-mode rejection ratio and input impedance are greatly improved.
Further, the impedance matching circuit 112 includes：First operational amplifier U1, the second operational amplifier U2,
Four resistance R4, the 5th resistance R5 and the 6th resistance R6；
The low-pass filter circuit 111 includes：First resistor R1, second resistance R2 and the first electric capacity C1；
The in-phase input end of the first operational amplifier U1 passes through first resistor R1 connection first electrodes（Shown in Fig. 2
For SEMG_Vin+）, the in-phase input end of the first operational amplifier U1 also passes through first electric capacity C1 the second operation amplifiers of connection
Device U2 in-phase input end；The output end of the first operational amplifier U1 is put by the 5th resistance R5 connections first computing
Big device U1 inverting input；The inverting input of the first operational amplifier U1 passes through the 4th resistance R4 the second computings of connection
Amplifier U2 inverting input, the inverting input of the first operational amplifier U1 also passes sequentially through the 4th resistance R4 and
Six resistance R6 the second operational amplifiers of connection U2 output end；The output end connection high-pass filtering of the first operational amplifier U1
Circuit 113；The in-phase input end of the second operational amplifier U2 passes through second resistance R2 connection second electrodes（Shown in Fig. 2
For SEMG_Vin-）；The output end connection high-pass filtering circuit 113 of the second operational amplifier U2.
Specifically, the first operational amplifier U1, the second operational amplifier U2 are electromyographic signal impedance matching input, and
First resistor R1, second resistance R2 and the first electric capacity C1 composition low-pass filter circuits, have wherein the first electric capacity C1 is employed
Compared with the X2Y electric capacity of high parameter symmetry, it is to avoid because single capacitor parameter unbalance causes circuit common-mode rejection ratio to decline.The present invention is
Meet requirement of the myoelectricity Acquisition Circuit to input impedance, in-phase input end is added before differential input end（Ideal situation is similarly hereinafter
Phase input impedance tends to be infinitely great）, improve its input impedance.Select the operation amplifier of amplifier first of common-mode rejection ratio Striking symmetry
Device U1 and the second operational amplifier U2, i.e. U1 and U2 are high input impedance, the double operational of high cmrr, so as to improve resistance
The gain A d1 of anti-match circuit；Bridge balance differential amplifier circuit 114 improves differential mode as close possible to bridge balance simultaneously
Gain.
Further, the high-pass filtering circuit 113 includes the 9th resistance R9, the tenth resistance R10, the 4th electric capacity C4 and the
Five electric capacity C5；
The bridge balance differential amplifier circuit 114 includes：12nd resistance R12, the 13rd resistance R13 and the 6th computing
Amplifier U6；
The in-phase input end of the 6th operational amplifier U6 passes through the 4th electric capacity C4 connections impedance matching circuit 112；Institute
The in-phase input end for stating the 6th operational amplifier U6 also passes sequentially through the 9th resistance R9 and the computing of the tenth resistance R10 connections the 6th is put
Big device U6 inverting input；The in-phase input end of the 6th operational amplifier U6 also pass sequentially through the 12nd resistance R12 and
The operational amplifier U6 of 13rd resistance R13 connections the 6th inverting input；The anti-phase input of the 6th operational amplifier U6
End passes through the 5th electric capacity C5 connections impedance matching circuit 112；The output end connection bandpass filtering of the 6th operational amplifier U6
Module 130（Fig. 2 show SEMG_1ST）.
Specifically, the 6th operational amplifier U6 be integrated instrument amplifier, that is, bridge balance differential amplification road, it is interior
Portion meets bridge balance and the symmetrical difference integrated circuit of parameter.9th resistance R9, the tenth resistance R10, the 4th electric capacity C4 and
Five electric capacity C5 constitute high-pass filtering circuit, filter out motion artifactses noise and because of muscular movement process electrode relative motion generation
Low frequency wonder noise, prevents it from causing bridge balance differential amplifier circuit to export saturation, high-pass filtering circuit -3dB, cut-off frequency
For fH=1/ (2 Π R9 C4).As shown in Fig. 2 the 6th operational amplifier U6 in-phase input end passes through the 4th electric capacity C4 connections
First operational amplifier U1 output end.6th operational amplifier U6 inverting input is transported by the 5th electric capacity C5 connections second
Calculate amplifier U2 output end.
Further, the conducting wire shield guard circuit 115 includes the 3rd operational amplifier U3；3rd computing is put
Big device U3 in-phase input end connection high-pass filtering circuit 113；The inverting input connection the of the 3rd operational amplifier U3
Three operational amplifier U3 output end；The output head grounding of the 3rd operational amplifier U3.Specifically, the 3rd operation amplifier
Device U3 is the conducting wire shield guard circuit of addition.As shown in Figure 2.The in-phase input end of the 3rd operational amplifier U3 passes through
The operational amplifier U6 of 9th resistance R9 connections the 6th in-phase input end；The in-phase input end of the 3rd operational amplifier U3 is also
Pass through the operational amplifier U6 of the tenth resistance R10 connections the 6th inverting input.
By foregoing circuit, front end amplification module of the invention, common-mode rejection ratio gathers national standard to altogether considerably beyond myoelectricity
The requirement of mould rejection ratio（National standard is more than 80dB）；Input impedance exceeds well over myoelectricity Standard（National standard is more than 100M）.
Further, the leg drive module 120 includes：3rd resistor R3, the 7th resistance R7, the 8th resistance R8,
11 resistance R11, the 6th electric capacity C6, four-operational amplifier U4 and the 5th operational amplifier U5；
The output end of the 5th operational amplifier U5 passes through the electrode of 3rd resistor R3 connections the 3rd（It is shown in Fig. 2
SEMG_RL）；The output end of the 5th operational amplifier U5 also passes through the anti-of the operational amplifier U5 of the 7th resistance R7 connections the 5th
Phase input, the output end of the 5th operational amplifier U5 also passes sequentially through the 6th electric capacity C6 and the 8th resistance R8 connections the 5th
Operational amplifier U5 inverting input；The in-phase input end ground connection of the 5th operational amplifier U5；
The output end of the four-operational amplifier U4 passes through the anti-of the operational amplifier U5 of the 11st resistance R11 connections the 5th
Phase input；The output end of the four-operational amplifier U4 is also connected with four-operational amplifier U4 inverting input；It is described
Four-operational amplifier U4 in-phase input end connection front end amplification module 110.
Specifically, the in-phase input end of the four-operational amplifier U4 connects the 6th computing by the 12nd resistance and put
Big device U6 in-phase input end, also passes through the operational amplifier U6 of the 13rd resistance R13 connections the 6th inverting input.The leg
Portion's drive module 120 is used for the collection of organism surface electric signal, and it is from two equal biasing resistors the tenth of pre-amplifying module
Human body common-mode voltage is taken out in the middle of two resistance R12, the 13rd resistance R13, successively through voltage follower U4 and inverting amplifier U5
Right leg is connected to, it plays repid discharge, effective attenuation human body common mode voltage signal equivalent to common-mode voltage Shunt negative feedback circuit
Effect.
In practical application, as shown in Fig. 2 the input/output relation derivation of myoelectricity front end amplification module is as follows：Assuming that A,
The current potential that 5 points of B, C, D and E is VA, VB, VC, VD, VE；VA-VB is differential electrode potential difference VI.VD=(1+2R5/R4)VA；
VE=(1+2R6/R4)VB；VD-VE=(1+2R5/R4)(VA-VB)=(1+2R5/R4)VI；Then Vout=K (1+2R5/R4) VI.
Then the electromyographic signal of collection is sequentially passed through the hair such as bandpass filtering modules block, power frequency filtration module by front end amplification module
Deliver to microprocessor.The problem of being directed to Hz noise carrier deviation when handling electromyographic signal, has used adaptive work
Frequency is filtered so that the electromyographic signal signal to noise ratio collected is high, can be used for follow-up myoelectricity and be assessed.
Further, the microprocessor 180 includes：
Pretreatment unit, for being pre-processed to the corresponding data signal of the surface electromyogram signal, obtains corresponding
Myoelectricity data；
Muscular strength assessment unit, for dividing selected part myoelectricity number from the myoelectricity data using end-point detection algorithm
According to according to the corresponding root mean square of part myoelectricity data calculating and myoelectric integral value, according to the root mean square and myoelectric integral value
Muscle tensility is carried out to assess and the assessment of muscular strength grade；
Muscular fatigue assessment unit, for dividing selected part myoelectricity from the myoelectricity data using end-point detection algorithm
Data, corresponding average frequency and median frequency are calculated according to the part myoelectricity data, according to the average frequency and intermediate value frequency
Rate carries out muscular fatigue assessment.
Specifically, in practical application, can be handled by microprocessor the corresponding data signal of electromyographic signal,
Also it can be transmitted to computer（PC）Handled.The data that above-mentioned hardware components are collected carry out one by USB transmission to computer
Column processing.
The flow handled in microprocessor or computer end is as follows, first to entering to the corresponding data signal of the surface electromyogram signal
Row pretreatment, obtains corresponding myoelectricity data.Software filtering namely is carried out to data signal, noise jamming is further removed.
Filtering process includes high-pass filtering, power frequency filtering and average filter.The high-pass filtering and average filter are all traditional filtering
Device, Technical comparing is ripe.The power frequency filtering uses the adaptive frequency filter of modified, and the wave filter is to be directed to grid disturbance
Noise carrier deviation problem and design.The signal being achieved in that can be used by further evaluation module well.For example
Data signal to the electromyographic signal after parsing carries out obtaining correspondence after bandpass filter, adaptive frequency filter etc. are pre-processed
Myoelectricity data.
Then electromyographic signal is extracted from background signal using end-point detection algorithm, referred to for calculating electromyographic signal items
Mark.Specifically read in from database after myoelectricity data, divided using end-point detection algorithm from background signal and choose useful flesh
Electric signal, extracts the segment data and calculates its evaluation index（Root mean square, myoelectric integral value, average frequency and median frequency）, then paint
Evaluation index curve processed.The end-point detection algorithm uses short-time energy given threshold, is aided with short-time average zero-crossing rate and is sentenced
It is disconnected.In the evaluation index, root mean square, electromyographic signal integrated value are used for Muscle tensility assessment, muscular strength grade and assessed, and average frequency
Rate, median frequency are assessed for muscular fatigue, and the slope of wherein median frequency fitting a straight line is defined as fatigue strength index.
Further, the muscular fatigue assessment unit includes：Myoelectricity data extract subelement, for utilizing end-point detection
Algorithm divides selected part myoelectricity data from the myoelectricity data；Muscular fatigue computation subunit, for utilizing Fourier in short-term
Leaf transformation calculates the electromyographic signal frequency spectrum of the part myoelectricity data, and the tired journey of muscle is obtained by the median frequency calculated
Degree.
Further, in computer end, rehabilitation training pattern can be also set, for example normal mode, resistance to force mode, explosive force mould
Formula and self-defined pattern, can add self-defined training mode, and the training mould of patient is adapted to according to motion muscle assessment result setting
Formula, increases the specific aim for the treatment of.Self-defined training mode is added in the rehabilitation modality of motion muscle.This pattern can basis
The result of assessment independently project training pattern, ensure that patient carries out rehabilitation training, and energy in muscle tolerance range
It is enough to carry out independent assortment for required rehabilitation parameter so that treatment is more targeted, contributes to the rehabilitation of patient.
It is illustrated below, rehabilitation training pattern is followed successively by：Normal mode, resistance to force mode, outburst force mode, self-defined mould
Formula.Rehabilitation training normal mode：When patient's myoelectricity level exceedes feedback threshold, screen ejection fireworks broadcast window is used as prize
Encourage.Wherein feedback threshold is to gather the electromyographic signal in patient first minute, take the 80% of maximum as myoelectricity feedback threshold
Value, when patient repeatedly makes great efforts also to be unable to reach threshold level, gradually reduce EMG feedback threshold value until patient successfully trigger for
Only.This training mode is applied to 2-4 grades of muscular strength patients.
Endurance training pattern：Patient needs to keep muscle output strength stable within a certain period of time to control to produce similar ladder
The electromyogram of shape.This training mode is higher for the patient requests of quadriplegia, and muscle strength level will be more than 3 grades.
Explosive force training mode：It is required that patient in yellow area rapid desufflation, relax one's muscles, allow myoelectricity curve to have quickly
Change procedure.The training mode mainly tests flexibility, the quick-reaction capability that nervous centralis is controlled muscle, to the flesh of user
Power level requirement is higher, at least more than 3 grades.
Self-defined training mode：The schema flexibility is high, can flexibly set feedback profile threshold according to rehabilitation assessment result
Value, sets any difficulty EMG feedback threshold curve, is favorably improved the specific aim for the treatment of.User can be clicked on by mouse and be set
The key point in region carries out feedback profile setting, when the myoelectricity level of user is more than or equal to feedback threshold, it will obtain certain
Reward on total mark, user, which sets click confirmation after feedback threshold curve shape, can enter training mode.
Based on above-mentioned surface myoelectric harvester, above-mentioned surface myoelectric harvester is used present invention also offers a kind of
Electromyographic signal processing method, as shown in figure 3, including：
S100, collection skin surface electromyographic signal, and be converted to corresponding data signal；
S200, the data signal is pre-processed, obtain corresponding myoelectricity data；
S300, using end-point detection algorithm selected part myoelectricity data are divided from the myoelectricity data, according to the part
Myoelectricity data calculate corresponding root mean square, myoelectric integral value, average frequency and median frequency；
S400, Muscle tensility assessment is carried out according to the root mean square and myoelectric integral value and muscular strength grade is assessed, according to described
Average frequency and median frequency carry out muscular fatigue assessment.
In summary, the present invention is provided a kind of surface myoelectric harvester and its electromyographic signal processing method, use impedance
The none-inverting input circuits of matching are combined with meeting the magnifier of bridge balance, improve the input of front end myoelectricity Acquisition Circuit
Impedance and common-mode rejection ratio；The work frequency circuit constituted using new integrated frequency filter, thermal noise is small, single stage filtering have compared with
High-performance；Using the adaptive frequency filter of modified, follow up grid disturbance in real time, effectively eliminates Hz noise；Using based on
Short-time energy and short-time zero-crossing rate method carry out electromyographic signal end-point detection, provide accurate fixed for electromyographic signal analysis and evaluation below
Position；Electromyographic signal frequency spectrum is calculated using short time discrete Fourier transform, the degree of fatigue of muscle is obtained by the median frequency calculated.
Myoelectricity fatigue is fed back into exercise for power again, reasonable arrangement training strength prevents muscle damage；Self-defined training mode is added,
It is adapted to the training mode of patient according to motion muscle assessment result setting, increases the specific aim for the treatment of；By the electromyographic signal of collection
Training is assessed with muscle to be combined, algorithm process is carried out using the electromyographic signal of extraction, and obtaining a series of muscular states is used for flesh
Tension force and muscular fatigue are assessed.Muscle tensility and muscular strength grade are assessed using myoelectricity root mean square and integrated value in myoelectricity assessment, is made
Muscular fatigue degree is assessed with average frequency and median frequency；And using assessment result as rehabilitation training guidance.
It should be appreciated that the application of the present invention is not limited to above-mentioned citing, for those of ordinary skills, can
To be improved or converted according to the above description, all these modifications and variations should all belong to the guarantor of appended claims of the present invention
Protect scope.
Claims (2)
1. a kind of surface myoelectric harvester, it is characterised in that including：
First electrode, second electrode and the 3rd electrode for obtaining skin surface electromyographic signal；
For the front end amplification module that myoelectricity differential signal is connected, extracted with first electrode and second electrode and is amplified；
Leg drive module for human body reference electrical signal to be connected, extracted with the 3rd electrode；
Bandpass filtering modules block for filtering out the signal beyond myoelectricity scope；
Power frequency filtration module for filtering out Hz noise；
Level lifting module for raising level；
For carrying out AD samplings to electromyographic signal, analog-to-digital conversion is the AD acquisition modules of corresponding data signal；
For the microprocessor handled data signal；
The digital isolation module interfered for reducing between AD acquisition modules and microprocessor；
The first electrode, second electrode connect bandpass filtering modules block by front end amplification module；3rd electrode passes through leg
Portion's drive module connection front end amplification module；The bandpass filtering modules block, power frequency filtration module, level lifting module, AD collections
Module, digital isolation module and microprocessor are sequentially connected；
The front end amplification module includes：Low-pass filter circuit, impedance matching circuit, high-pass filtering circuit, conducting wire shielding are driven
Dynamic circuit and bridge balance differential amplifier circuit；
The impedance matching circuit connects first electrode and second electrode respectively by low-pass filter circuit；The bridge balance is poor
Divide amplifying circuit to pass through leg drive module and connect the 3rd electrode；The impedance matching circuit connects electricity by high-pass filtering circuit
Bridge balanced differential amplifying circuit；The conducting wire shield guard circuit connects the high-pass filtering circuit；The bridge balance is poor
Divide amplifying circuit connection bandpass filtering modules block；
The microprocessor includes：
Pretreatment unit, for being pre-processed to the corresponding data signal of the surface electromyogram signal, obtains corresponding myoelectricity
Data；
Muscular strength assessment unit, for dividing selected part myoelectricity data, root from the myoelectricity data using end-point detection algorithm
Corresponding root mean square and myoelectric integral value are calculated according to the part myoelectricity data, flesh is carried out according to the root mean square and myoelectric integral value
Tension force is assessed and muscular strength grade is assessed；
Muscular fatigue assessment unit, for dividing selected part myoelectricity number from the myoelectricity data using end-point detection algorithm
According to according to the corresponding average frequency of part myoelectricity data calculating and median frequency, according to the average frequency and median frequency
Carry out muscular fatigue assessment；
The pretreatment unit is additionally operable to carrying out software filtering, including height to the corresponding data signal of the surface electromyogram signal
Pass filter, power frequency filtering and average filter, the power frequency filtering use the adaptive frequency filter of modified, to electromyographic signal
The problem of being directed to Hz noise carrier deviation during processing, has used adaptive power frequency to filter so that the myoelectricity letter collected
Number signal to noise ratio height, can be used for follow-up myoelectricity and assess；
The power frequency filtration module is in series by two integrated frequency filters, and the chip heat of the integrated frequency filter is made an uproar
Sound is small, and single stage filtering depth is deep；
The impedance matching circuit includes：First operational amplifier, the second operational amplifier, the 4th resistance, the 5th resistance and
Six resistance；
The low-pass filter circuit includes：First resistor, second resistance and the first electric capacity；
The in-phase input end of first operational amplifier connects first electrode, first operational amplifier by first resistor
In-phase input end also pass through the in-phase input end of first the second operational amplifier of capacitance connection；First operational amplifier
Output end connects the inverting input of first operational amplifier by the 5th resistance；First operational amplifier it is anti-phase
Input connects the inverting input of the second operational amplifier, the anti-phase input of first operational amplifier by the 4th resistance
End also passes sequentially through the 4th resistance and the 6th resistance connects the output end of the second operational amplifier；First operational amplifier
Output end connects high-pass filtering circuit；The in-phase input end of second operational amplifier passes through the electricity of second resistance connection second
Pole；The output end connection high-pass filtering circuit of second operational amplifier；
The high-pass filtering circuit includes the 9th resistance, the tenth resistance, the 4th electric capacity and the 5th electric capacity；
The bridge balance differential amplifier circuit includes：12nd resistance, the 13rd resistance and the 6th operational amplifier；
The in-phase input end of 6th operational amplifier passes through the 4th capacitance connection impedance matching circuit；6th computing is put
The in-phase input end of big device also passes sequentially through the 9th resistance and the tenth resistance connects the inverting input of the 6th operational amplifier；Institute
The in-phase input end for stating the 6th operational amplifier also passes sequentially through the 12nd resistance and the 13rd resistance the 6th operation amplifier of connection
The inverting input of device；The inverting input of 6th operational amplifier passes through the 5th capacitance connection impedance matching circuit；Institute
State the output end connection bandpass filtering modules block of the 6th operational amplifier；
The conducting wire shield guard circuit includes the 3rd operational amplifier；The in-phase input end of 3rd operational amplifier connects
Connect high-pass filtering circuit；The inverting input of 3rd operational amplifier connects the output end of the 3rd operational amplifier；It is described
The output head grounding of 3rd operational amplifier；
The leg drive module includes：3rd resistor, the 7th resistance, the 8th resistance, the 11st resistance, the 6th electric capacity, the 4th
Operational amplifier and the 5th operational amplifier；
The output end of 5th operational amplifier connects the 3rd electrode by 3rd resistor；5th operational amplifier it is defeated
Go out the inverting input that end also connects the 5th operational amplifier by the 7th resistance, the output end of the 5th operational amplifier is also
Pass sequentially through the 6th electric capacity and the 8th resistance connects the inverting input of the 5th operational amplifier；5th operational amplifier
In-phase input end is grounded；
The output end of the four-operational amplifier connects the inverting input of the 5th operational amplifier by the 11st resistance；Institute
The output end for stating four-operational amplifier is also connected with the inverting input of four-operational amplifier；The four-operational amplifier
In-phase input end connects front end amplification module；
The muscular fatigue assessment unit includes：
Myoelectricity data extract subelement, for dividing selected part myoelectricity number from the myoelectricity data using end-point detection algorithm
According to；
Muscular fatigue computation subunit, the electromyographic signal frequency for calculating the part myoelectricity data using short time discrete Fourier transform
Spectrum, the degree of fatigue of muscle is obtained by the median frequency calculated.
2. a kind of electromyographic signal processing method using surface myoelectric harvester as claimed in claim 1, it is characterised in that
Including：
Step S1, collection skin surface electromyographic signal, and be converted to corresponding data signal；
Step S2, the data signal is pre-processed, obtain corresponding myoelectricity data；
Step S3, using end-point detection algorithm selected part myoelectricity data are divided from the myoelectricity data, according to the part flesh
Electric data calculate corresponding root mean square, myoelectric integral value, average frequency and median frequency；
Step S4, Muscle tensility assessment is carried out according to the root mean square and myoelectric integral value and muscular strength grade is assessed, according to described flat
Equal frequency and median frequency carry out muscular fatigue assessment；
In the step S1, pass through low-pass filter circuit, impedance matching circuit, high-pass filtering circuit, conducting wire shield guard electricity
Road and bridge balance differential amplifier circuit collection skin surface electromyographic signal；
In the step S2, pretreatment is carried out to the data signal to be included with high-pass filtering, power frequency filtering and average filter pair
Data signal is filtered, and the power frequency filtering uses the adaptive frequency filter of modified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510211190.6A CN104799854B (en) | 2015-04-29 | 2015-04-29 | A kind of surface myoelectric harvester and its electromyographic signal processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510211190.6A CN104799854B (en) | 2015-04-29 | 2015-04-29 | A kind of surface myoelectric harvester and its electromyographic signal processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104799854A CN104799854A (en) | 2015-07-29 |
| CN104799854B true CN104799854B (en) | 2017-10-24 |
Family
ID=53685375
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510211190.6A Active CN104799854B (en) | 2015-04-29 | 2015-04-29 | A kind of surface myoelectric harvester and its electromyographic signal processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104799854B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109222957A (en) * | 2018-08-29 | 2019-01-18 | 西安电子科技大学 | A kind of automation ECG measuring system |
Families Citing this family (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105125211A (en) * | 2015-09-28 | 2015-12-09 | 李继有 | Myoelectricity collection and display device |
| CN105232040A (en) * | 2015-09-29 | 2016-01-13 | 武汉工程大学 | Muscle fatigue detection system and method |
| CN106344012A (en) * | 2015-11-16 | 2017-01-25 | 闽南师范大学 | Sport fatigue electromyographic signal acquisition device |
| CN107233094A (en) * | 2016-03-29 | 2017-10-10 | 上海海神医疗电子仪器有限公司 | A kind of electromyogram evoked potentuial measuring system |
| CN105997067B (en) * | 2016-06-21 | 2018-09-21 | 中国计量大学 | Adaptive electromyography signal detection process method based on fraction Fourier conversion |
| CN106291114B (en) * | 2016-08-25 | 2019-02-01 | 三诺生物传感股份有限公司 | A kind of ac impedance measurement circuit of blood glucose meter, method and device |
| CN106175761B (en) * | 2016-09-22 | 2023-04-25 | 中国科学院合肥物质科学研究院 | Touch detection device for synchronously detecting touch stimulus force and electromyographic signals |
| CN106419894B (en) * | 2016-10-11 | 2019-08-02 | 江苏南大五维电子科技有限公司 | One kind acquiring front-end processing circuit for electro-physiological signals |
| CN106361291A (en) * | 2016-10-20 | 2017-02-01 | 天津大学 | Preset level raised orthogonal sine wave frequency encoding hyperspectral imaging system |
| CN106344040A (en) * | 2016-10-20 | 2017-01-25 | 天津大学 | Measuring device and measuring method for raised level square wave modulation photoelectric volume pulse wave |
| CN107137080A (en) * | 2017-05-25 | 2017-09-08 | 中国科学院深圳先进技术研究院 | Chronic back pain patient muscle's active state determination methods and system |
| CN107678275B (en) * | 2017-08-10 | 2021-05-11 | 深圳市海浦蒙特科技有限公司 | Frequency converter for film blowing machine and grammage control method thereof |
| CN107822630B (en) * | 2017-09-28 | 2020-06-02 | 广州博厚医疗技术有限公司 | Myoelectric signal detection circuit of rehabilitation system and rehabilitation system |
| CN107510454A (en) * | 2017-10-11 | 2017-12-26 | 兰州交通大学 | Myoelectric signal collection apparatus and system based on multistage filtering |
| CN107874763A (en) * | 2017-12-11 | 2018-04-06 | 无锡智开医疗机器人有限公司 | A kind of lower limb data collecting system |
| CN108681396B (en) * | 2018-04-28 | 2021-07-06 | 北京机械设备研究所 | Human-computer interaction system and method based on brain-myoelectricity bimodal neural signals |
| CN109316165A (en) * | 2018-09-30 | 2019-02-12 | 北京中医药大学东直门医院 | A kind of wearable Chinese medicine diagnosis equipment |
| CN109394230A (en) * | 2018-12-07 | 2019-03-01 | 北京大学深圳研究生院 | A kind of gait analysis system based on surface electromyogram signal |
| CN109464144B (en) * | 2018-12-25 | 2024-04-16 | 东莞晋杨电子有限公司 | Handheld myoelectricity acquisition device |
| CN109464145A (en) * | 2018-12-25 | 2019-03-15 | 东莞晋杨电子有限公司 | A kind of myoelectricity acquisition device |
| CN109602398A (en) * | 2019-01-28 | 2019-04-12 | 深兰科技（上海）有限公司 | A kind of physical signs monitoring device |
| JPWO2021075247A1 (en) * | 2019-10-17 | 2021-04-22 | ||
| CN112773380B (en) * | 2019-11-07 | 2023-09-22 | 深圳市理邦精密仪器股份有限公司 | Myoelectric signal processing method, processing equipment and storage medium |
| CN111387972B (en) * | 2020-04-20 | 2023-10-31 | 广州易风健康科技股份有限公司 | Weak current signal acquisition circuit and seat ring and toilet cover with same |
| CN112353368B (en) * | 2020-05-08 | 2022-03-22 | 北京理工大学 | Multi-input signal epileptic seizure detection system based on feedback adjustment |
| CN112006686A (en) * | 2020-07-09 | 2020-12-01 | 浙江大学 | Neck muscle fatigue analysis method and system |
| CN112617866A (en) * | 2020-12-31 | 2021-04-09 | 深圳美格尔生物医疗集团有限公司 | EMG (electromagnetic EMG) electromyographic signal digital acquisition circuit and system |
| CN112933408B (en) * | 2021-02-02 | 2022-12-23 | 深圳市康进医疗科技有限公司 | System and method for collecting surface electromyographic signals and controlling low-frequency electrical stimulation |
| CN115299950A (en) * | 2022-08-12 | 2022-11-08 | 歌尔股份有限公司 | Electromyographic signal acquisition circuit, wearable device and control method |
| CN115778398B (en) * | 2022-11-11 | 2023-08-08 | 中节能风力发电股份有限公司 | Body surface electromyographic signal detection circuit and detection method |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4136684A (en) * | 1977-02-07 | 1979-01-30 | Scattergood Mark G | Linear electromyographic biofeedback system |
| DE10353969B4 (en) * | 2003-11-19 | 2007-06-21 | Bernhard Gleich | Bio-signal measurement system |
| CN201727511U (en) * | 2010-07-20 | 2011-02-02 | 上海理工大学 | Multichannel surface myoelectricity collecting and analyzing system |
| CN102646149A (en) * | 2011-02-18 | 2012-08-22 | 上海理工大学 | Information data management method of patient suffering from muscle dysfunction |
| CN203290900U (en) * | 2013-06-06 | 2013-11-20 | 郑义 | Wireless surface myoelectricity collecting instrument based on Bluetooth |
| CN103405228B (en) * | 2013-08-21 | 2015-12-23 | 中国科学院自动化研究所 | A kind of portable cardiac and surface myoelectric measuring device |
| CN104510461B (en) * | 2013-09-26 | 2018-09-28 | 中国科学院深圳先进技术研究院 | Digital full isolation ecg signal acquiring denoising device and its method |
| CN104224169A (en) * | 2014-10-14 | 2014-12-24 | 沈阳工程学院 | Surface electromyogram signal linear analyzing method for judging human body muscle fatigue |
| CN204181614U (en) * | 2014-11-03 | 2015-03-04 | 孙会楠 | Based on DSP Electro-cadiogram signals detector system |
-
2015
- 2015-04-29 CN CN201510211190.6A patent/CN104799854B/en active Active
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109222957A (en) * | 2018-08-29 | 2019-01-18 | 西安电子科技大学 | A kind of automation ECG measuring system |
| CN109222957B (en) * | 2018-08-29 | 2020-11-10 | 西安电子科技大学 | Automatic ECG measuring system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104799854A (en) | 2015-07-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104799854B (en) | A kind of surface myoelectric harvester and its electromyographic signal processing method | |
| CN102426651B (en) | Human body forearm surface electromyogram signal acquisition and pattern recognition system | |
| CN101658425B (en) | Device and method for detecting attention focusing degree based on analysis of heart rate variability | |
| CN1089011C (en) | Functional neuromyo-electric signal identification and stimulation apparatus | |
| CN104323773B (en) | A kind of ERG signal acquiring and processing system based on EOG and method | |
| CN103300853B (en) | Diagnosis and treatment system based on surface myoelectricity | |
| WO2020118797A1 (en) | Prosthesis control method, apparatus, system and device, and storage medium | |
| CN111696645A (en) | Hand exoskeleton rehabilitation training device and method based on surface electromyographic signals | |
| CN107822629B (en) | Method for detecting myoelectric axes on surfaces of limbs | |
| CN108261197A (en) | Upper limb healing evaluation system and method based on surface myoelectric and motion module | |
| CN107894965A (en) | A kind of coupled processing method for being used for two groups of signal with different type | |
| Zhang et al. | Low-cost circuit design of EEG signal acquisition for the brain-computer interface system | |
| Zhou et al. | A data-driven volitional EMG extraction algorithm during functional electrical stimulation with time variant parameters | |
| CN113288181A (en) | Individual template reconstruction method based on steady-state visual evoked potential electroencephalogram signal identification | |
| Veer | Experimental study and characterization of SEMG signals for upper limbs | |
| CN204654934U (en) | A kind of healing hand function assessment and training system | |
| CN101138495B (en) | Electrostimulation noise elimination instrument | |
| CN205083477U (en) | Real time control device based on body surface flesh | |
| Veer | A flexible approach for segregating physiological signals | |
| CN107233092A (en) | A kind of processing method for heart physiological electric signal | |
| Carra et al. | Evaluation of sensorimotor rhythms to control a wheelchair | |
| CN209203258U (en) | A kind of myoelectricity acquisition device | |
| CN205832393U (en) | A kind of music and EMG feedback treatment clothes | |
| Pravin et al. | Extraction, Processing and Analysis of Surface Electromyogram Signal and Detection of Muscle Fatigue Using Machine Learning Methods | |
| CN106102575A (en) | A kind of myoelectric signal collection apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |