CN109259762A - A kind of muscular fatigue comprehensive test device based on multivariate data fusion - Google Patents
A kind of muscular fatigue comprehensive test device based on multivariate data fusion Download PDFInfo
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- CN109259762A CN109259762A CN201811300981.6A CN201811300981A CN109259762A CN 109259762 A CN109259762 A CN 109259762A CN 201811300981 A CN201811300981 A CN 201811300981A CN 109259762 A CN109259762 A CN 109259762A
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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
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
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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
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
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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
- A61B5/48—Other medical applications
- A61B5/4884—Other medical applications inducing physiological or psychological stress, e.g. applications for stress testing
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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
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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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
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
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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
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Abstract
The invention discloses a kind of muscular fatigue comprehensive test devices based on multivariate data fusion, including data acquisition module, data transmission module and data analysis and evaluation and test module, pass through blood oxygen transducer, surface myoelectric sensor and piezoelectric transducer acquire oxygen saturation signal respectively, surface electromyogram signal and muscle vibration signal, and it is handled by single-chip microcontroller and data is passed to computer, host computer muscular fatigue evaluating software based on exploitation, Time-Frequency Analysis is carried out to three kinds of signals, using variable quantity weighted average method, it is accurate to obtain muscular fatigue comprehensive evaluating index, it provides muscular fatigue under different motion state and determines result；The device is easy to operate, high reliablity, can be widely applied to human muscle and moves evaluation and test and rehabilitation training field.
Description
Technical field
The present invention relates to the evaluation technology fields of muscular fatigue, are based on blood oxygen saturation, surface flesh more particularly to one kind
Electric signal and muscle vibration signal are the muscular fatigue assessment device of multiple variable datas fusion.
Background technique
Muscular fatigue refers to that body muscle bears the fatigue that work activities generate, and muscle is under ability to work under fatigue state
Drop, or even will cause muscle damage, thus can effective evaluation fatigue to the intensity and fortune of accurate monitoring sufferer rehabilitation training
The exercise intensity important in inhibiting of mobilization.
Traditional evaluation index of muscular fatigue has lactate threshold mensuration, ventilation threshold (Ventilator
Threshold, VT) mensuration and be based on surface myoelectric (Surface Electromyogram, sEMG) mensuration, still
Blood lactase acid test needs to acquire blood sample, can not real-time perfoming detection；VT measuring device is complicated, expensive, and needs to wear when test
The comfort of breathing mask, tester is poor.It should not be detected in training.
For surface myoelectric mensuration, under existing research, relatively accurately muscular fatigue can be detected, still
Surface electromyogram signal acquisition is not easy, and sensor surface needs to apply conductive pattern layer, and the resistance of human skin surface is kept using solution
It is constant.So can be monitored with surface myoelectric to fatigue in static occasion, but in the occasion of some movements, should not make
With this method, other methods is needed to be evaluated and tested.
For muscle in fatigue, in addition to surface electromyogram signal can change, there are also the vibration frequencies of muscle, blood oxygen saturation
It can change etc. multinomial physical signs, but since these physiological signals are more faint, find feature simultaneously in the research of time domain
It is unobvious, whether tired it can not entirely accurate define muscle；Therefore, urgent need, which develops one kind, can be adapted for various occasions especially
The device that muscular fatigue is evaluated and tested when being rehabilitation training.
Summary of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides it is a kind of with blood oxygen saturation, muscle signals and
The comprehensive system tested and assessed to muscular fatigue of surface electromyogram signal.
The present invention includes being made of three kinds of blood oxygen transducer, surface myoelectric sensor and piezoelectric transducer biosensors
Data acquisition module, SCM Based data processing and transmission module are melted using signal Time-Frequency Analysis and multivariate data
Data analysis and assessment module and the upper computer software interface of conjunction；By to blood oxygen saturation, muscle signals, surface electromyogram signal
Time-Frequency Analysis is carried out, the median frequency for drawing three kinds of signals changes with time figure；Utilize the intermediate value frequency of three kinds of physiological signals
The feature that rate changes over time provides a muscular fatigue comprehensive evaluation index, when the overall target reaches a certain specified value,
Thus judge that muscle is in a state of fatigue.
The blood oxygen transducer, using the blood oxygen heart rate sensor based on photoplethymograph, by red-light LED and red
Outer smooth LED transmitting feux rouges and infrared light, receive reflected light by the photoelectric transformer on sensor after tissue and blood vessel,
This signal is converted into current signal and is amplified and is exported, the current value of the feux rouges and infrared light that are just reflected, from
And processing and analysis after being convenient for.
The piezoelectric transducer is flesh sound sensor, which collects flesh using the piezoelectric effect of piezoelectric ceramics
The vibration signal of meat, and electric signal is converted to, by built-in amplifying circuit and filter circuit, removal is generated due to human motion
Noise, obtain muscle signals.
The surface myoelectric sensor acquires mind by electrode slice using the surface myoelectric sensor of triple channel
Bioelectrical signals through muscle systems.There are three electrode slices in each channel of the sensor, for inhibiting common-mode signal, and can
To carry out the acquisition of three groups of data simultaneously, and then obtain electromyography signal.
The data acquisition module can measure oxygen saturation signal of the muscle nearby in artery, at muscle simultaneously
Muscle signals and surface electromyogram signal.
The data processing and transmission module receives the physiological signal from data acquisition module, first just according to human body
Three groups of signals are filtered enhanced processing respectively, remove clutter by the cutoff frequency of normal physiological signal frequency setting filter
Interference, recycle single-chip microcontroller to be AD converted, computer be passed to by USB serial ports or bluetooth, consequently facilitating carrying out data
Analysis.
The method that the data analysis mainly uses Time-Frequency Analysis with assessment module, respectively believes blood oxygen saturation
Number, muscle signals and surface electromyogram signal analyzed, on frequency domain, calculate blood oxygen saturation from starting to each moment
MFSpO2With attenuation rate λMFSpO2, median frequency MF of the muscle signals in each persistently equal long periodsMMGWith attenuation rate λMFMMG, table
Median frequency MF of the facial muscle electric signal in each persistently equal long periodssEMGWith attenuation rate λMFsEMG.Using average weighted side
Method establishes a muscular fatigue comprehensive evaluation index, according to the different numerical value of comprehensive evaluation index, can determine whether muscle is in
Fatigue state.
The upper computer software interface, it is characterised in that: oxygen saturation signal, flesh message can be gone out with real-time exhibition
Number, surface electromyogram signal and muscular fatigue composite rating index change with time；And it can be comprehensive according to muscular fatigue
The numerical value of evaluation index judges that muscle starts the state of tired and complete fatigue, and makes prompt.
Compared with prior art, present invention has an advantage that with oxygen saturation signal, muscle signals and surface myoelectric
Signal many index synthesis tests and assesses to muscular fatigue, avoid single signal detection it is possible that false judgment, can
More accurately to judge the state of muscular fatigue.Present invention selection is analyzed and is handled on frequency domain to physiological data, and
It is not that feature is mixed and disorderly and the time domain vulnerable to external interference, signal characteristic can be made convenient for extracting.The present invention is adapted to different inspections
Conditions and environment is surveyed, can be applied in the middle of more extensive field.
Detailed description of the invention
Fig. 1 is a kind of muscular fatigue comprehensive test apparatus function block diagram based on multivariate data fusion.
Fig. 2 is a kind of muscular fatigue comprehensive test device hardware configuration schematic diagram based on multivariate data fusion.
Fig. 3 is a kind of muscular fatigue comprehensive test software schematic diagram based on multivariate data fusion.
Specific embodiment
As shown in Figure 1, the muscular fatigue comprehensive test device based on multivariate data fusion mainly includes being sensed by blood oxygen
Data acquisition module, the SCM Based number of device, three kinds of biosensors of surface myoelectric sensor and piezoelectric transducer composition
According to processing and transmission module, the data analysis merged using signal Time-Frequency Analysis and multivariate data and the module and upper of testing and assessing
Machine software interface.
As shown in Fig. 2, 1 is blood oxygen transducer, 2 be piezoelectric transducer, and 3 be surface myoelectric sensor, and 4 transmit for data
Module, that is, single-chip microcontroller accesses computer by USB interface or bluetooth module, 5 for data analyze with module of testing and assessing be based on it is upper
The signal Time-Frequency Analysis system of machine.
Blood oxygen transducer as shown in Figure 2 in order to keep data more accurate, needs to place the sensors on hand when in use
It is measured on finger or at wrist, red-light LED, infrared light LED and photoelectric transformer is close to skin, fixed with black sticky cloth,
Avoid interference of the external light source to experimental data.In data acquisition, needs to guarantee that light path is constant, the variation of light path is avoided to cause
The variation of reflected light.
Piezoelectric transducer as shown in Figure 2, when in use, it should the flesh of fatigue is easier to when sensor is close to and is tested
Meat to guarantee the proper motion of muscle avoids that muscle damage occurs.
Surface myoelectric sensor as shown in Figure 2, when in use, by three electrode tip connection electrode pieces, in order to inhibit table
The common mode interference of facial muscle electric signal needs for electrode slice to be attached on muscle in sequence.It should be noted that surface electromyogram signal
More faint, electrode is preferably attached to the strongest belly of muscle portion of myoelectricity granting amount, to reduce the interference for closing on muscle；The epidermis of test needs
It is carried out disinfection with alcohol, otherwise the dust of epidermis or sweat can interfere surface signal.
Data processing and transmission module as depicted in figs. 1 and 2 is the data output interface that will connect three sensors
Low-pass filter circuit is first passed around, the cutoff frequency of low-pass filter is set according to the normal frequency of physiology signal, it will be outer
The High-frequency Interference on boundary filters out.Amplifier is set, faint physiological signal is enlarged into more tractable signal.After filter and amplification
Data output interface and power interface it is corresponding with the data-interface of single-chip microcontroller be connected, data are transmitted by single-chip microcontroller, will
Three groups of signal values are output in computer by USB interface or wireless blue tooth biography.
Data analysis as depicted in figs. 1 and 2 and assessment module, can be to blood oxygen saturation, muscle signals, surface myoelectric
Signal carries out being further processed for data, and is fused to muscular fatigue comprehensive evaluation index, by muscular fatigue comprehensive evaluation index
It is compared with specified value, can judge whether muscle is in a state of fatigue.
Muscular fatigue comprehensive test software as shown in Figure 3 based on multivariate data fusion, including oxygen saturation signal,
Change with time figure, time of muscle signals, the median frequency of surface electromyogram signal and muscular fatigue composite rating index is aobvious
Show, start the display lamp of testing button, " starting fatigue " and " fatigue completely ".
Blood oxygen saturation median frequency as shown in Figure 3 changes module, is the feux rouges and infrared light that will receive computer
Current value handled, be filtered first, with low-pass filtering and average value filtering, filter the larger signal of error and
High-frequency Interference.The light transmittance for recycling oxyhemoglobin different with infrared light to feux rouges with hemoglobin, can be by reflected light
Current value obtain blood oxygen saturation, handled with current value of the Lang Bo-Beer law to feux rouges and infrared light,
The blood oxygen saturation is obtained in the value of each sampled point, to oxygen saturation signal from starting to each moment by above formula
Carry out Fast Fourier Transform (FFT) respectively, calculate the value of power spectral density, thus obtain blood oxygen saturation from starting to every
The median frequency MF at a momentSpO2Attenuation rate λ with the median frequency in each stage relative to initial median frequencyMFSpO2。
The median frequency MF that will be calculatedSpO2Using the time as abscissa, the variation of blood oxygen saturation median frequency is made
Figure, the blood oxygen saturation median frequency for being shown in the muscular fatigue comprehensive test software based on multivariate data fusion change module
In.
Muscle signals median frequency as shown in Figure 3 changes module, is that the muscle signals that will be received are handled, first
The electric signal of muscle vibration is extracted, then Fast Fourier Transform (FFT) is carried out to each signal continued in the isometric period, is calculated
Median frequency MF of the muscle signals in each lasting isometric periodMMGMedian frequency with each stage is relative to initial
The attenuation rate λ of median frequencyMFMMG.The median frequency MF that will be calculatedMMGIt maps, is shown in based on more by abscissa of the time
In the muscle signals median frequency variation module of the muscular fatigue comprehensive test software of variable data fusion.
Electromyography signal median frequency as shown in Figure 3 changes module, is the surface myoelectric letter in three channels that will be received
It number extracts, extracts the surface electromyogram signal in the channel that measured uses, each is continued the isometric time to electromyography signal
Signal in section carries out Fast Fourier Transform (FFT), calculates median frequency MFsEMGMedian frequency with each stage is relative to initial
The attenuation rate λ of median frequencyMFsEMG.The median frequency MF that will be calculatedsEMGIt maps by abscissa of the time, is shown in and is based on
In the electromyography signal median frequency variation module of the muscular fatigue comprehensive test software of multivariate data fusion.
Muscular fatigue comprehensive evaluation index module as shown in Figure 3, is the evaluation index of three groups of signals according to shown in table 1
Data fusion is carried out with average weighted method, wherein according to the amplitude of variation of three kinds of physiological signals and to muscular states
Influence power, blood oxygen saturation occupies 40%, and muscle signals occupy 20%, and electromyography signal occupies 40%, establishes muscular fatigue synthesis and comments
Valence index λMF。
Muscular fatigue comprehensive evaluation index λMFIt can change with the variation of training time, using the time as abscissa, can make
Muscular fatigue comprehensive evaluation index λMFChange with time figure, works as λMFWhen abruptly increase occurs and being greater than 0.6, muscle starts fatigue；When
λMFWhen being steadily 0.68, muscle is in as completely tired state.
Training module as shown in Figure 3 is clicked " starting to train ", and the time starts timing, and in the blood oxygen saturation in left side
The variation of value frequency, the variation of muscle signals median frequency, the variation of electromyography signal median frequency, muscular fatigue comprehensive evaluation index go out
Existing figure line, works as λMFWhen abruptly increase occurs and being greater than 0.6, muscle starts fatigue, and " starting fatigue " on host computer, indicator light lights；When
λMFWhen being steadily 0.68, it is completely tired state that muscle, which is in, and " fatigue completely " indicator light lights, should stop on host computer
Training.
Embodiment
According to the application method of above-mentioned each sensor, the light source of blood oxygen transducer and photoelectric transformer are tightly attached to tested
At the finger of person, piezoelectric transducer and surface electromyogram signal sensor are close at muscle, and connection sensor is corresponding with single-chip microcontroller
Data-interface and power interface, the output interface of single-chip microcontroller is connected with the USB interface of computer, open muscular fatigue assessment
Software.
Allow measured to hold dumbbell 2.75kg, forearm is vertical with large arm holding, and with ground keeping parallelism, click " start
Training ", records start time and static a period of time.At this time it can be seen that blood oxygen saturation, flesh sound on the upper computer interface
Signal, surface electromyogram signal median frequency in reduction over time, while muscular fatigue overall target is also changing,
Work as λMFWhen abruptly increase occurs and being greater than 0.6, muscle starts fatigue, and " starting fatigue " on host computer, indicator light lights, the record time and
The impression of tester；Work as λMFWhen being steadily 0.68, it is completely tired state that muscle, which is in, and " fatigue completely " indicates on host computer
Lamp lights, and records the impression of time and tester, deconditioning；Under resting state, continue to test, blood oxygen saturation, flesh sound
Signal, surface electromyogram signal median frequency rise to original state, tester has rested completion at this time.
It is described above and illustrate basic principle of the invention, specific implementation process and advantages of the present invention, technology in the industry
Personnel are not it should be appreciated that the present invention is limited by above-described embodiment, in the premise for not departing from spirit of that invention and scope of design
Under, the present invention will have various improvement and expand, these are improved each falls in scope of the claimed invention with expansion, the present invention
Claimed range is defined by the appending claims and its equivalent thereof.
Claims (2)
1. a kind of muscular fatigue comprehensive test device based on multivariate data fusion, it is characterised in that: including being saturated by blood oxygen
The data acquisition module for spending sensor, three kinds of biosensors of surface myoelectric sensor and piezoelectric transducer composition, is based on monolithic
The data processing and transmission module of machine, and based on the data of signal Time-Frequency Analysis and multivariate data fusion method analysis with
Assessment module.
2. the method that data analysis according to claim 1 uses signal Time-Frequency Analysis with evaluation and test module, by sensor
The data measured carry out time frequency analysis, calculate separately median frequency MF of the blood oxygen saturation from starting to each momentSpO2With
Attenuation rate λMFSpO2, median frequency MF of the muscle signals in each persistently equal long periodsMMGWith attenuation rate λMFMMG, surface myoelectric letter
Number each persistently equal long periods median frequency MFsEMGWith attenuation rate λMFsEMG；It is calculated by multivariable weighted average method
Muscular fatigue composite rating index λMF=0.4λMFSpO2+0.2λMFMMG+0.4λMFsEMG；Work as λMFWhen abruptly increase occurs and being greater than 0.6, flesh
Meat starts fatigue, and " starting fatigue " on host computer, indicator light lights；Work as λMFWhen being steadily 0.68, it is completely tired that muscle, which is in,
State, " fatigue completely " indicator light lights on host computer, and issues deconditioning warning.
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| CN110179643A (en) * | 2019-03-21 | 2019-08-30 | 华东师范大学 | A kind of neck rehabilitation training system and training method based on annulus sensor |
| CN111449641A (en) * | 2020-04-20 | 2020-07-28 | 浙江大学 | Evaluation device and evaluation method for muscle function state based on photoelectric signal detection |
| CN111657957A (en) * | 2020-07-02 | 2020-09-15 | 中国医学科学院生物医学工程研究所 | Time-frequency joint analysis-based lip muscle strength analysis method and system |
| CN112263254A (en) * | 2020-06-11 | 2021-01-26 | 复旦大学附属华山医院 | Human body energy consumption prediction system based on surface electromyogram signal sensor and prediction method thereof |
| CN112807002A (en) * | 2019-11-18 | 2021-05-18 | 深圳市理邦精密仪器股份有限公司 | Parameter optimization method, system, equipment and storage medium of muscle training instrument |
| CN113143241A (en) * | 2020-12-07 | 2021-07-23 | 未来穿戴技术有限公司 | Device and method for detecting health state of human muscle and massage equipment |
| CN114366589A (en) * | 2022-01-21 | 2022-04-19 | 珠海格力电器股份有限公司 | Control method and control device for massage equipment, storage medium and electronic device |
| CN114403904A (en) * | 2021-12-31 | 2022-04-29 | 北京津发科技股份有限公司 | Device for determining muscle state based on electromyographic signals and muscle blood oxygen saturation |
| CN114504334A (en) * | 2022-02-07 | 2022-05-17 | 苏州微创畅行机器人有限公司 | State prediction method, state prediction device, computer equipment and storage medium |
| CN114931390A (en) * | 2022-05-06 | 2022-08-23 | 电子科技大学 | Muscle force estimation method based on fatigue analysis |
| CN114504334B (en) * | 2022-02-07 | 2024-04-26 | 苏州微创畅行机器人有限公司 | State prediction method, state prediction device, computer equipment and storage medium |
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| CN110179643A (en) * | 2019-03-21 | 2019-08-30 | 华东师范大学 | A kind of neck rehabilitation training system and training method based on annulus sensor |
| CN112807002A (en) * | 2019-11-18 | 2021-05-18 | 深圳市理邦精密仪器股份有限公司 | Parameter optimization method, system, equipment and storage medium of muscle training instrument |
| CN111449641A (en) * | 2020-04-20 | 2020-07-28 | 浙江大学 | Evaluation device and evaluation method for muscle function state based on photoelectric signal detection |
| CN111449641B (en) * | 2020-04-20 | 2021-07-20 | 浙江大学 | Evaluation device and evaluation method for muscle function state based on photoelectric signal detection |
| CN112263254A (en) * | 2020-06-11 | 2021-01-26 | 复旦大学附属华山医院 | Human body energy consumption prediction system based on surface electromyogram signal sensor and prediction method thereof |
| CN111657957A (en) * | 2020-07-02 | 2020-09-15 | 中国医学科学院生物医学工程研究所 | Time-frequency joint analysis-based lip muscle strength analysis method and system |
| CN113143241A (en) * | 2020-12-07 | 2021-07-23 | 未来穿戴技术有限公司 | Device and method for detecting health state of human muscle and massage equipment |
| CN114403904A (en) * | 2021-12-31 | 2022-04-29 | 北京津发科技股份有限公司 | Device for determining muscle state based on electromyographic signals and muscle blood oxygen saturation |
| CN114403904B (en) * | 2021-12-31 | 2023-01-13 | 北京津发科技股份有限公司 | Device for determining muscle state based on electromyographic signals and muscle blood oxygen saturation |
| WO2023123864A1 (en) * | 2021-12-31 | 2023-07-06 | 北京津发科技股份有限公司 | Muscle state determining apparatus based on electromyographic signal and muscle blood oxygen saturation |
| CN114366589A (en) * | 2022-01-21 | 2022-04-19 | 珠海格力电器股份有限公司 | Control method and control device for massage equipment, storage medium and electronic device |
| CN114504334A (en) * | 2022-02-07 | 2022-05-17 | 苏州微创畅行机器人有限公司 | State prediction method, state prediction device, computer equipment and storage medium |
| CN114504334B (en) * | 2022-02-07 | 2024-04-26 | 苏州微创畅行机器人有限公司 | State prediction method, state prediction device, computer equipment and storage medium |
| CN114931390A (en) * | 2022-05-06 | 2022-08-23 | 电子科技大学 | Muscle force estimation method based on fatigue analysis |
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Application publication date: 20190125 |