CN106388819A - Human upper limb muscle state monitoring system based on surface electromyogram signals and judging method thereof - Google Patents
Human upper limb muscle state monitoring system based on surface electromyogram signals and judging method thereof Download PDFInfo
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- 210000001364 upper extremity Anatomy 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000012544 monitoring process Methods 0.000 title claims abstract description 16
- 210000003205 muscle Anatomy 0.000 title claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 49
- 239000013598 vector Substances 0.000 claims abstract description 8
- 230000003387 muscular Effects 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 19
- 238000001914 filtration Methods 0.000 claims description 18
- 238000000605 extraction Methods 0.000 claims description 17
- 238000012546 transfer Methods 0.000 claims description 13
- 230000003183 myoelectrical effect Effects 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 12
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- 210000000352 storage cell Anatomy 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 238000003909 pattern recognition Methods 0.000 claims description 6
- 230000003321 amplification Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 5
- 239000000203 mixture Substances 0.000 claims description 5
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 5
- 229920000742 Cotton Polymers 0.000 claims description 4
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 4
- 229910021607 Silver chloride Inorganic materials 0.000 claims description 4
- 239000002003 electrode paste Substances 0.000 claims description 4
- HKZLPVFGJNLROG-UHFFFAOYSA-M silver monochloride Chemical compound [Cl-].[Ag+] HKZLPVFGJNLROG-UHFFFAOYSA-M 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000007781 pre-processing Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 5
- 230000003247 decreasing effect Effects 0.000 description 2
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- 235000013372 meat Nutrition 0.000 description 2
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- 210000004556 brain Anatomy 0.000 description 1
- 230000007177 brain activity Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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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]
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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Abstract
The invention discloses a human upper limb muscle state monitoring system based on surface electromyogram signals and a judging method thereof. The monitoring system comprises a signal collection module, a signal processing module and a mode classifying module, wherein the signal collection module is used for collecting human upper limb surface electromyogram signals and converting the human upper limb surface electromyogram signals into digital signals; the signal processing module is used for preprocessing the digital signals and extracting features; the mode classifying module is used for identifying input feature vectors, outputting types of the feature vectors and judging the upper limb muscle state. The judging method comprises the following steps: collecting human surface electromyogram signals via the signal collection module; processing the human surface electromyogram signals via the signal processing module; and identifying modes of the human surface electromyogram signals via the mode classifying module. Through the adoption of the scheme, effective electromyogram signals are efficiently extracted and are accurately analyzed and processed; the cost is reduced; the calculation quantity is greatly reduced; the real-time performance of the system is improved; the system has a high application value in the market.
Description
Technical field
The present invention relates to medical science, more particularly, to, a kind of human upper limb flesh based on surface electromyogram signal
Meat condition monitoring system and determination methods.
Background technology
Surface electromyogram signal（sEMG）It is the potential of the skin surface of the muscle measuring.By amplifying, filtering and sample
Process, sEMG can be adjusted to suitable data segment, then maintenance data treatment technology carries out feature extraction to it.Using point
Class device carries out pattern recognition to the feature extracted, and can determine whether the state of human muscle, including muscle carried out which kind of action, with
And whether muscle is in fatigue state.Judge which kind of action muscle is carrying out, the control of exoskeleton equipment can be entered by sEMG
System.Virtual reality technology can also be utilized, control the equipment in virtual scene.Judge whether muscle is in fatigue state, for
Judge human body working condition, make great sense especially for athlete or work high above the ground personnel.
Find through the retrieval to prior art, Chinese invention patent publication No.：CN105361880A, date of publication：
On March 2nd, 2016, title：The identifying system of muscular movement event and its method.A kind of muscular movement event of this disclosure of the invention
Identifying system and method.Signal acquisition module that system is made up of myoelectricity acquisition module and brain wave acquisition module, signal processing
Module and signal identification module collectively constitute.Using the method for electromyographic signal and EEG signals comprehensive analysis, comprise the following steps：
Collection musculation analogue signal and brain activity analogue signal；The signal of collection is processed and carried out incident detection；
Identification labeled bracketing are simulated to the event detecting.This invention can be in nervous physiology detection and the high criterion of diagnostic field
Remember and effective electromyographic signal, and the myoelectricity and EEG signals event of institute's labelling is accurately analyzed and processed.But, this invention
Collection electromyographic signal and EEG signals, not only increase cost, and increased amount of calculation simultaneously, and impact system ONLINE RECOGNITION is real
Shi Xing.Therefore, prior art existing defects, need to improve.
Content of the invention
The technical problem to be solved is to provide a kind of new human upper limb flesh based on surface electromyogram signal
Meat condition monitoring system and determination methods.
For achieving the above object, the present invention is employed following technical schemes：A kind of people based on surface electromyogram signal
Body muscle of upper extremity condition monitoring system, including signal acquisition module, signal processing module and mode classification module；Described signal is adopted
Collection module is used for gathering human upper limb surface electromyogram signal, and converts analog signals into digital signal, sends signal processing to
Module；The digital signal that described signal processing module is used for signal acquisition module is sent carries out pretreatment and feature extraction；Institute
State mode classification module for recognizing to the characteristic vector inputting, and by the type output belonging to these characteristic vectors, sentence
Disconnected muscle of upper extremity state；
Described signal acquisition module includes power supply unit of voltage regulation, sensor unit, analogue signal amplifying unit, bandpass filtering list
Unit, A/D converting unit and data transfer unit, described power supply unit of voltage regulation is connected with the unit of signal acquisition module respectively
Connect, described sensor unit, analogue signal amplifying unit, bandpass filtering unit, A/D converting unit and data transfer unit are successively
Connect；
Described power supply unit of voltage regulation is used for for 220 volts of alternating voltages being converted to 5 volts of voltages of High-accuracy direct current, is described signals collecting
Module provides burning voltage；
Described sensor unit is used for gathering electro-physiological signals produced by musculation；
Described analogue signal amplifying unit is used for being amplified processing by the surface electromyogram signal collecting analog quantity, it is to avoid myoelectricity
The excessively faint impact of signal amplitude；
Described bandpass filtering unit is used for being filtered processing by the surface electromyogram signal analog quantity after amplifying, choose by 20 ~
The signal of 450Hz, eliminates noise jamming；
Described A/D converting unit, for being converted to digital quantity by the surface electromyogram signal collecting analog quantity；
Described data transfer unit, is carried out for quickly giving signal processing module by the surface electromyogram signal digital quantity after processing
The process of next step.
Preferably, in the described human upper limb muscular states monitoring system based on surface electromyogram signal, at described signal
Reason module includes Signal Pretreatment unit, sampling unit, feature extraction unit and data storage cell；Described Signal Pretreatment list
Unit is amplified for the surface electromyogram signal sending signal acquisition module, Filtering Processing；Described sampling unit is used for number
According to being screened, choose suitable data segment；Described feature extraction unit, for extracting average and the root-mean-square of sampled data section
Value；Described data storage cell, is stored for the data after processing above-mentioned sampling unit, is used for off-line analysiss.
Preferably, in the described human upper limb muscular states monitoring system based on surface electromyogram signal, described pattern is divided
Generic module includes pattern classifier unit and artificial neural network training unit, and described pattern classifier unit is used for surface flesh
Signal characteristics are combined judging, which kind of action the upper limb telling user is carrying out；Described artificial neural network instruction
Practice unit to be used for the surface electromyogram signal feature and corresponding upper limks movements mode combinations multiple training samples of composition, and complete right
The training of pattern classifier.
Preferably, in the described human upper limb muscular states monitoring system based on surface electromyogram signal, described pattern is divided
Generic module also includes output unit, the muscular states that described output unit identifies for output mode grader unit.
The determination methods of the human upper limb muscular states based on surface electromyogram signal, comprise the following steps：
S1, gathers human body surface myoelectric signal by signal acquisition module；
S2, is processed to human body surface myoelectric signal by signal processing module；
S3, carries out pattern recognition by mode classification module to human body surface myoelectric signal.
Preferably, in the described determination methods based on the human upper limb muscular states of surface electromyogram signal, in step S1
In, the sensor unit of described signal acquisition module is made up of multiple Ag/AgCl surface electrodes, using before should first use medical alcohol
Cotton by human body skin location for paste wiped clean, then by electrode paste on skin；The surface electromyogram signal collecting is through simulation letter
Number amplifying unit is amplified processing, it is to avoid the excessively faint impact of electromyographic signal amplitude；Surface electromyogram signal mould after amplification
Analog quantity, through bandpass filtering unit, is filtered processing, chooses the signal by 20 ~ 450Hz.Filtered signal is changed through A/D
Unit, analog quantity is converted to digital quantity；Surface electromyogram signal digital quantity through data transfer unit, by USB transmission to signal
Processing module.
Preferably, in the described determination methods based on the human upper limb muscular states of surface electromyogram signal, in step S3
Include：
Step S31, offline by surface electromyogram signal feature and the corresponding upper limks movements mode combinations multiple training samples of composition, and
Complete the training to pattern classifier；
Step S32, the eigenvalue exporting feature extraction unit online are input in pattern classifier, are combined judging, point
Which kind of action the upper limb discerning user is carrying out.
Step S33, on computer display screen output mode grader judged result.
It is that, using such scheme, the present invention is in bio-signal acquisition and identification neck with respect to the beneficial effect of prior art
The effective electromyographic signal of domain high efficiency extraction, and it is accurately analyzed and processed, cost-effective, amount of calculation is greatly decreased, improves
System real time, has good market using value.
Brief description
Fig. 1 is the block schematic illustration of one embodiment of the present of invention；
Fig. 2 is the flow chart of Fig. 1 embodiment of the present invention.
Specific embodiment
For the ease of understanding the present invention, below in conjunction with the accompanying drawings and specific embodiment, the present invention will be described in more detail.
The preferred embodiment of the present invention is given in accompanying drawing.But, the present invention can realize in many different forms, does not limit
Embodiment described by this specification.On the contrary, providing the purpose of these embodiments to make to the disclosure
Understand more thoroughly comprehensive.
It should be noted that when element is referred to as " being fixed on " another element, it can be directly on another element
Or can also there is element placed in the middle.When an element is considered as " connection " another element, it can be directly connected to
To another element or may be simultaneously present centering elements.Term " connection " that this specification is used, " level ",
"left", "right" and similar statement are for illustrative purposes only.
Unless otherwise defined, all of technology that this specification is used and scientific terminology are led with the technology belonging to the present invention
The implication that the technical staff in domain is generally understood that is identical.The term being used in the description of the invention in this specification is simply
The purpose of description specific embodiment, is not intended to limit the present invention.
As shown in Figure 1 and Figure 2, one embodiment of the present of invention is, is somebody's turn to do the human upper limb muscle shape based on surface electromyogram signal
State monitoring system, including signal acquisition module 1, signal processing module 2 and mode classification module 3；Described signal acquisition module 1 is used
In collection human upper limb surface electromyogram signal, and convert analog signals into digital signal, send signal processing module to；Described
The digital signal that signal processing module 2 is used for signal acquisition module is sent carries out pretreatment and feature extraction；Described pattern is divided
Generic module 3 is used for the characteristic vector of input is recognized, and by the type output belonging to these characteristic vectors, judges upper limb flesh
Meat-like state；
Described signal acquisition module 1 includes power supply unit of voltage regulation, sensor unit 11, analogue signal amplifying unit 12, band logical filter
Ripple unit 13, A/D converting unit 14 and data transfer unit 15, described power supply unit of voltage regulation is each with signal acquisition module respectively
Individual unit connects, described sensor unit 11, analogue signal amplifying unit 12, bandpass filtering unit 13, A/D converting unit 14 and
Data transfer unit 15 is sequentially connected；Described power supply unit of voltage regulation is used for for 220 volts of alternating voltages being converted to 5 volts of High-accuracy direct current
Voltage, provides burning voltage for described signal acquisition module；Described sensor unit is used for gathering life produced by musculation
The reason signal of telecommunication；Described analogue signal amplifying unit is used for being amplified processing by the surface electromyogram signal collecting analog quantity, keeps away
Exempt from the excessively faint impact of electromyographic signal amplitude；Described bandpass filtering unit is used for the surface electromyogram signal analog quantity after amplifying
It is filtered processing, chooses the signal by 20 ~ 450Hz, eliminate noise jamming；Described A/D converting unit, for collecting
Surface electromyogram signal analog quantity be converted to digital quantity；Described data transfer unit, for by process after surface electromyogram signal
Digital quantity quickly gives the process that signal processing module carries out next step.
Preferably, described signal processing module 2 includes Signal Pretreatment unit 21, sampling unit 22, feature extraction unit
23 and data storage cell 24；The surface electromyogram signal that described Signal Pretreatment unit is used for sending signal acquisition module is carried out
Amplification, Filtering Processing；Described sampling unit is used for being screened data, chooses suitable data segment；Described feature extraction list
Unit, for extracting average and the root-mean-square value of sampled data section；Described data storage cell, for processing above-mentioned sampling unit
Data afterwards is stored, and uses for off-line analysiss.
Preferably, described mode classification module 3 includes pattern classifier unit 32 and artificial neural network training unit 31,
Described pattern classifier unit is used for being combined judging by surface electromyogram signal feature, the upper limb telling user enters
Row is which kind of action；Described artificial neural network training unit is used for surface electromyogram signal feature and corresponding upper limks movements pattern
Combination constitutes multiple training samples, and completes the training to pattern classifier.Preferably, described based on surface electromyogram signal
In human upper limb muscular states monitoring system, described mode classification module also includes output unit 33, and described output unit is used for
The muscular states that output mode grader unit identifies.
Present invention achieves reducing the purpose of system cost, and decreasing operational data, reducing operand, computing speed
Rate improves more than 40% compared with prior art, and system power dissipation reduces by 30% compared with prior art.Make the recognition efficiency of related muscles state
It is greatly improved, improve the real-time of system.
The determination methods of the human upper limb muscular states based on surface electromyogram signal, comprise the following steps：
S1, gathers human body surface myoelectric signal by signal acquisition module；
S2, is processed to human body surface myoelectric signal by signal processing module；
S3, carries out pattern recognition by mode classification module to human body surface myoelectric signal.
Preferably, in step sl, the sensor unit of described signal acquisition module is by multiple Ag/AgCl surface electrode groups
Become, using before should first use medical alcohol cotton by human body skin location for paste wiped clean, then by electrode paste on skin；Collect
Surface electromyogram signal through analogue signal amplifying unit be amplified process, it is to avoid the excessively faint impact of electromyographic signal amplitude；
Surface electromyogram signal analog quantity after amplification, through bandpass filtering unit, is filtered processing, chooses the signal by 20 ~ 450Hz.
Filtered signal, through A/D converting unit, analog quantity is converted to digital quantity；Surface electromyogram signal digital quantity transmits single through data
Unit, by USB transmission to signal processing module.
Preferably, include in step s3：
Step S31, offline by surface electromyogram signal feature and the corresponding upper limks movements mode combinations multiple training samples of composition, and
Complete the training to pattern classifier；
Step S32, the eigenvalue exporting feature extraction unit online are input in pattern classifier, are combined judging, point
Which kind of action the upper limb discerning user is carrying out.
Step S33, on computer display screen output mode grader judged result.
The determination methods being somebody's turn to do the human upper limb muscular states based on surface electromyogram signal specifically include following steps：
Step 1, collection human body surface myoelectric signal；The present embodiment is realized by signal acquisition module, this signal acquisition module by
Power supply unit of voltage regulation, sensor unit, analogue signal amplifying unit, bandpass filtering unit, A/D converting unit, data transmission is single
Unit's composition；
Described power supply unit of voltage regulation, for 220 volts of alternating voltages are converted to 5 volts of voltages of High-accuracy direct current, is described signal
Acquisition module provides burning voltage；
Described sensor unit is made up of multiple Ag/AgCl surface electrodes, using before should first use medical alcohol cotton by human body skin
Skin location for paste wiped clean, then by electrode paste on skin；
The surface electromyogram signal collecting is amplified processing through analogue signal amplifying unit, it is to avoid electromyographic signal amplitude is excessively micro-
Weak impact；
Surface electromyogram signal analog quantity after amplification, through bandpass filtering unit, is filtered processing, chooses by 20 ~ 450Hz's
Signal；
Filtered signal, through A/D converting unit, analog quantity is converted to digital quantity；
Surface electromyogram signal digital quantity through data transfer unit, by USB transmission to computer.
Step 2, human body surface myoelectric signal is processed；The present embodiment is realized by signal processing module, this signal
By Signal Pretreatment unit, sampling unit, feature extraction unit, data storage cell forms processing module；
It is transferred to the surface electromyogram signal of computer through data transfer unit, through Signal Pretreatment unit, signal is put
Greatly, Filtering Processing；
Pretreated signal, through sampling unit, data is screened, and chooses suitable data segment；
Signal one side after sampling, through feature extraction unit, extracts average and the root-mean-square value of sampled data section；Warp simultaneously
Cross data storage cell, the data after sampling is stored, use for off-line analysiss.
Step 3, pattern recognition is carried out to human body surface myoelectric signal；The present embodiment is realized by mode classification module, should
Mode classification module is made up of pattern classifier unit, artificial neural network training unit.
Described pattern recognition, specially：Step 1, offline by surface electromyogram signal feature and corresponding upper limks movements mould
Formula combination constitutes multiple training samples, and completes the training to pattern classifier；Step 2, online feature extraction unit is exported
Eigenvalue be input in pattern classifier, be combined judging, which kind of action the upper limb telling user carrying out；Step
Rapid 3, output mode grader judged result on computer display screen.
It should be noted that above-mentioned each technical characteristic continues to be mutually combined, form the various embodiments not being enumerated above,
It is accordingly to be regarded as the scope of description of the invention record；And, for those of ordinary skills, can add according to the above description
To improve or to convert, and all these modifications and variations all should belong to the protection domain of claims of the present invention.
Claims (7)
1. a kind of human upper limb muscular states monitoring system based on surface electromyogram signal is it is characterised in that include signals collecting
Module, signal processing module and mode classification module；Described signal acquisition module is used for gathering human upper limb surface electromyogram signal,
And convert analog signals into digital signal, send signal processing module to；Described signal processing module is used for signals collecting
The digital signal that module is sent carries out pretreatment and feature extraction；Described mode classification module is used for the characteristic vector of input is entered
Row identification, and by the type output belonging to these characteristic vectors, judge muscle of upper extremity state；
Described signal acquisition module includes power supply unit of voltage regulation, sensor unit, analogue signal amplifying unit, bandpass filtering list
Unit, A/D converting unit and data transfer unit, described power supply unit of voltage regulation is connected with the unit of signal acquisition module respectively
Connect, described sensor unit, analogue signal amplifying unit, bandpass filtering unit, A/D converting unit and data transfer unit are successively
Connect；
Described power supply unit of voltage regulation is used for for 220 volts of alternating voltages being converted to 5 volts of voltages of High-accuracy direct current, is described signals collecting
Module provides burning voltage；
Described sensor unit is used for gathering electro-physiological signals produced by musculation；
Described analogue signal amplifying unit is used for being amplified processing by the surface electromyogram signal collecting analog quantity, it is to avoid myoelectricity
The excessively faint impact of signal amplitude；
Described bandpass filtering unit is used for being filtered processing by the surface electromyogram signal analog quantity after amplifying, choose by 20 ~
The signal of 450Hz, eliminates noise jamming；
Described A/D converting unit, for being converted to digital quantity by the surface electromyogram signal collecting analog quantity；
Described data transfer unit, is carried out for quickly giving signal processing module by the surface electromyogram signal digital quantity after processing
The process of next step.
2. the human upper limb muscular states monitoring system based on surface electromyogram signal according to claim 1, its feature exists
In described signal processing module includes Signal Pretreatment unit, sampling unit, feature extraction unit and data storage cell；Institute
State Signal Pretreatment unit be amplified for the surface electromyogram signal of sending signal acquisition module, Filtering Processing；Described adopt
Sample unit is used for being screened data, chooses suitable data segment；Described feature extraction unit, for extracting sampled data section
Average and root-mean-square value；Described data storage cell, is stored for the data after processing above-mentioned sampling unit, is used for
Off-line analysiss are used.
3. the human upper limb muscular states monitoring system based on surface electromyogram signal according to claim 2, its feature exists
In described mode classification module includes pattern classifier unit and artificial neural network training unit, described pattern classifier list
For being combined judging surface electromyogram signal feature, which kind of action the upper limb telling user is carrying out for unit；Described
Artificial neural network training unit is used for for surface electromyogram signal feature constituting multiple instructions with corresponding upper limks movements mode combinations
Practice sample, and complete the training to pattern classifier.
4. the human upper limb muscular states monitoring system based on surface electromyogram signal according to claim 3, its feature exists
In described mode classification module also includes output unit, and described output unit identifies for output mode grader unit
Muscular states.
5. the determination methods of the human upper limb muscular states based on surface electromyogram signal are it is characterised in that comprise the following steps：
S1, gathers human body surface myoelectric signal by signal acquisition module；
S2, is processed to human body surface myoelectric signal by signal processing module；
S3, carries out pattern recognition by mode classification module to human body surface myoelectric signal.
6. the determination methods of the human upper limb muscular states based on surface electromyogram signal according to claim 5, its feature
It is, in step sl, the sensor unit of described signal acquisition module is made up of multiple Ag/AgCl surface electrodes, using front
Medical alcohol cotton should first be used by human body skin location for paste wiped clean, then by electrode paste on skin；The surface flesh collecting
The signal of telecommunication is amplified processing through analogue signal amplifying unit, it is to avoid the excessively faint impact of electromyographic signal amplitude；After amplification
Surface electromyogram signal analog quantity, through bandpass filtering unit, is filtered processing, and chooses the signal by 20 ~ 450Hz, filtered
Signal, through A/D converting unit, analog quantity is converted to digital quantity；Surface electromyogram signal digital quantity, through data transfer unit, passes through
USB transmission is to signal processing module.
7. the determination methods of the human upper limb muscular states based on surface electromyogram signal according to claim 6, its feature
It is, include in step s3：
Step S31, offline by surface electromyogram signal feature and the corresponding upper limks movements mode combinations multiple training samples of composition, and
Complete the training to pattern classifier；
Step S32, the eigenvalue exporting feature extraction unit online are input in pattern classifier, are combined judging, point
Which kind of action the upper limb discerning user is carrying out；
Step S33, on computer display screen output mode grader judged result.
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| CN116439693A (en) * | 2023-05-18 | 2023-07-18 | 四川大学华西医院 | Gait detection method and system based on FMG |
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| CN112115813A (en) * | 2020-08-31 | 2020-12-22 | 深圳市联合视觉创新科技有限公司 | Human body electromyographic signal labeling method and device and computing equipment |
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| CN112101298A (en) * | 2020-10-15 | 2020-12-18 | 福州大学 | Gesture recognition system and method based on muscle electrical impedance signals |
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| CN113157095A (en) * | 2021-04-23 | 2021-07-23 | 上海交通大学 | Embedded real-time self-adaptive control method and system based on surface electromyogram signal |
| CN114974486A (en) * | 2022-04-18 | 2022-08-30 | 北京舱宇科技有限公司 | Processing method and system device for mass biological signal feedback data |
| CN114639460A (en) * | 2022-05-16 | 2022-06-17 | 天津医科大学眼科医院 | Cycloplegic demand prediction and paralysis post-diopter refractive state prediction method |
| CN116439693A (en) * | 2023-05-18 | 2023-07-18 | 四川大学华西医院 | Gait detection method and system based on FMG |
| CN116439693B (en) * | 2023-05-18 | 2024-05-28 | 四川大学华西医院 | Gait detection method and system based on FMG |
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