CN114748079A - Wearable myoelectric method for online evaluation of muscle movement fatigue degree - Google Patents
Wearable myoelectric method for online evaluation of muscle movement fatigue degree Download PDFInfo
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- CN114748079A CN114748079A CN202210286508.7A CN202210286508A CN114748079A CN 114748079 A CN114748079 A CN 114748079A CN 202210286508 A CN202210286508 A CN 202210286508A CN 114748079 A CN114748079 A CN 114748079A
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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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/251—Means for maintaining electrode contact with the body
- A61B5/256—Wearable electrodes, e.g. having straps or bands
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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
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/296—Bioelectric electrodes therefor specially adapted for particular uses for 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
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6828—Leg
Abstract
The invention discloses a wearable electromyography method for evaluating muscle movement fatigue on line, belonging to the technical field of analysis and evaluation of human body bioelectricity signals, comprising the following steps: carrying out ankle joint rehabilitation training on a tested person aiming at the lower limb rehabilitation robot, and preparing the recovery training and myoelectric signal acquisition of the tested person; evaluating muscle fatigue degree characteristic parameter selection and extraction; collecting an online electromyographic signal; the method is based on a portable electromyographic signal acquisition instrument, an online acquisition and online muscle fatigue evaluation system is developed secondarily, real-time data acquisition is completed through a device communication protocol by utilizing a Labview2020 platform, and hybrid programming is performed by combining the advantages of fast data processing, simple operation and the like of the Matlab platform, so that the functions of data waveform display, data storage, online processing and online evaluation are met, the muscle fatigue condition in the exercise production process can be conveniently and quickly known, and the muscle fatigue condition can be timely processed.
Description
Technical Field
The invention relates to the technical field of analysis and evaluation of human body bioelectricity signals, in particular to a wearable electromyography method for evaluating muscle movement fatigue on line.
Background
Muscle fatigue is a common physiological phenomenon in human life, has a wide range from muscle fatigue per se, can generate muscle fatigue along with the increase of production working strength and time, and is clinically manifested by slow down of action speed, reduced coordination and flexibility, muscle injury can occur after long-time accumulation, and muscle pain and even muscle atrophy can occur when production activities are carried out. In order to avoid muscle damage due to muscle fatigue, the degree of muscle fatigue should be effectively evaluated. The traditional muscle fatigue assessment is a method for estimating exercise intensity and fatigue proposed by Borg, and the used indexes are psychology and physiology, so that fatigue detection cannot be timely and effectively carried out.
Muscle tissue metabolites increase during muscle fatigue, and certain physiological changes occur, such as muscle fiber conduction velocity. Because muscle contraction is accompanied with the generation of the electromyographic signals, the electromyographic signals comprise muscle activity and physiological state information, and meanwhile, the surface electromyographic signals can be obtained through the surface electrodes. Therefore, in recent years, many scholars have paid attention to the evaluation of the degree of muscle fatigue by analyzing the electromyographic signals. At present, fatigue degree of muscles is evaluated through surface electromyographic signals, and after data processing is generally carried out on sEMG, characteristic parameter extraction is carried out to find out characteristics capable of accurately expressing muscle fatigue.
At present, most researchers adopt wired equipment to store the collected electromyographic signals and then analyze the muscle fatigue degree in an off-line manner, and the method is not timely; in addition, factors such as physical quality, nutritional status, sex, age, etc. of each person have a significant influence on muscle fatigue, and thus a fatigue evaluation method for each person should be established.
Therefore, a wearable electromyography method for evaluating the muscle movement fatigue on line is needed to be designed.
Disclosure of Invention
The invention aims to provide a wearable electromyography method for online evaluation of muscle movement fatigue, which aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a wearable myoelectric method for evaluating muscle movement fatigue on line comprises the following steps:
s1: the lower limb rehabilitation robot is used for carrying out ankle rehabilitation training on a testee, and preparing the rehabilitation training and myoelectric signal acquisition of the testee;
s2: evaluating muscle fatigue degree characteristic parameter selection and extraction;
s3: and collecting an online electromyographic signal.
Further, in the wearable electromyography method for online evaluation of muscle movement fatigue, the step S1 includes:
Selecting a plurality of different healthy testees, cleaning the acquisition part with alcohol before acquisition, placing two sensors at the same tibialis anterior muscle and rectus femoris part of each tester, but having less influence on the tibialis rectus muscle when the ankle joint is single-acting, thus not carrying out fatigue evaluation and reducing the interference of external uncertain factors; when the testee carries out rehabilitation training, the animation in the upper computer is opened at the same time, the influence of subjective fatigue is eliminated, and then the testee is carried out rehabilitation training and myoelectric signal acquisition.
Further, in the wearable electromyography method for online evaluation of muscle movement fatigue, the specific steps of S2 are:
respectively selecting a Waveform Length (WL) from the time domain characteristics, selecting a Wavelet Singular Entropy (WSE) from the nonlinear characteristics, extracting the characteristics of the collected surface electromyographic signals by using two characteristic parameters, adding the characteristics, analyzing the data change characteristics, determining the fatigue coefficient value corresponding to the muscle fatigue of an individual according to the data change rule, and taking the fatigue coefficient value as the basis; when the rehabilitation training is carried out on a testee, a large amount of sample data needs to be acquired, if the myoelectric signal in a certain effective time period is lower than the fatigue coefficient determined by an individual in advance through parameters extracted by WL and WSE, the fatigue state is judged at the moment, and the upper computer immediately reminds the rehabilitation training personnel to stop acquiring so as to ensure the reliability of the acquired data and the self safety of the patient.
Further, in the wearable electromyography method for online evaluation of muscle movement fatigue, the specific steps of S3 are:
the upper computer system integrates electromyographic signal acquisition, data preprocessing, feature extraction, data storage, waveform display and fatigue state reminding; in the rehabilitation process, the limb on one healthy side of a patient performs corresponding actions to actively control the lower limb rehabilitation robot, the ankle joint of the limb on one affected side of the patient is driven to move up and down, the tibialis anterior muscle contraction is generated during each ankle joint movement of the limb on one healthy side, at the moment, the disposable electrode plate is in contact with the skin of a human body, the electromyographic signals are transmitted to the portable electromyographic signal acquisition instrument, data are transmitted to the multifunctional upper computer programmed by Labview2020 and Matlab in a mixed mode through the Bluetooth receiver, and real-time storage, display, data processing and fatigue on-line evaluation of the electromyographic signals are achieved.
Further, in the wearable electromyography method for online evaluation of muscle exercise fatigue, the healthy subjects select different ages, different sexes, different heights, different weights and the like for proving the stability of the wearable method for online evaluation of muscle fatigue; when testing muscle fatigue possibly caused by different production activities, selecting the muscle tissue with the strongest relevance, wherein the selection method is more convincing to the evaluation of the muscle fatigue state of the human body.
Further, in the wearable electromyography method for online evaluation of muscle exercise fatigue, the time domain waveform length and the nonlinear wavelet singular entropy are used for evaluating the characteristic parameter of the muscle fatigue degree.
Furthermore, in the wearable electromyography method for online evaluation of muscle movement fatigue, the angle displacement range of the lower limb ankle joint pedal of the lower limb rehabilitation robot is (-7), the sole of the tested person is attached to the pedal of the rehabilitation robot, the pedal moves up and down to drive the sole of the tested person to move up and down, and the action mainly carries out rehabilitation training on the ankle joint; when a tested person carries out ankle joint rehabilitation training on the lower limb rehabilitation robot, the electromyographic signals are transmitted to a computer by a portable electromyographic signal acquisition instrument and are compared with the fatigue coefficient of the tibialis anterior muscle of the individual through characteristic parameter extraction to obtain the muscle fatigue degree evaluation result.
Furthermore, in the wearable electromyography method for online evaluation of muscle movement fatigue, the portable electromyography signal acquisition instrument is connected with two buttons of a disposable silver/silver chloride electrode, and the other side of the disposable silver/silver chloride electrode is connected with human skin through conductive adhesive.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention carries out online fatigue degree evaluation aiming at muscle tissues with strong relevance caused by specific actions, selects a portable acquisition instrument to carry out electromyographic signal acquisition in the fatigue evaluation process, liberates the range of the detected muscle fatigue actions, gets rid of the limitation of wired acquisition of the electromyographic signals, determines the personal fatigue characteristic coefficient about the tibialis anterior muscles by analyzing the extracted characteristic value, can carry out fatigue detection on the relevant muscle tissues in real time when a testee carries out online rehabilitation training by utilizing the coefficient, and has more timeliness compared with the conventional technology that the muscle fatigue evaluation is carried out offline mostly.
2. According to the invention, two types of software, namely Labview2020 and Matlab, are used for mixed programming, so that the real-time change condition of the electromyographic signals in the movement process can be realized, the goodwill of a tested person can be reminded when muscle fatigue occurs, and the requirements of quick fatigue detection result and high accuracy are met.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the online identification of the present invention;
FIG. 2 is a schematic view of the on-line fatigue evaluation result of the tibialis anterior of the subject of the present invention;
FIG. 3 is an interface diagram of an upper computer for online training of a personal wireless electromyography acquisition instrument according to the invention;
FIG. 4 is a diagram showing the rehabilitation training process of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that: a wearable myoelectric method for evaluating muscle movement fatigue on line comprises the following steps:
s1: carrying out ankle joint rehabilitation training on a tested person aiming at the lower limb rehabilitation robot, and preparing the recovery training and myoelectric signal acquisition of the tested person;
selecting a plurality of different healthy subjects (from the practical application perspective, eight healthy subjects with different ages, heights and sexes are selected, wherein the eight subjects comprise 6 males and 2 females, the ages are 23-30 years old, the heights are 160-180 centimeters long), cleaning the acquisition part with alcohol before acquisition, and placing two sensors at the same tibialis anterior muscle and the rectus femoris of each subject, but the tibialis rectus muscle has smaller influence when the ankle joint is singly moved, so that fatigue evaluation is not carried out, and the interference of external uncertain factors is reduced; when the testee carries out rehabilitation training, the animation in the upper computer is opened at the same time, the influence of subjective fatigue is eliminated, and then the testee is carried out rehabilitation training and electromyographic signal acquisition. The healthy testers select different ages, different sexes, heights, weights and the like and are used for verifying the stability of the wearable method for online muscle fatigue evaluation; when testing muscle fatigue possibly caused by different production activities, selecting the muscle tissue with the strongest relevance, wherein the selection method is more convincing to the evaluation of the muscle fatigue state of the human body.
S2: evaluating muscle fatigue degree characteristic parameter selection and extraction;
respectively selecting Waveform Length (WL) from time domain characteristics, selecting Wavelet Singular Entropy (WSE) from nonlinear characteristics, extracting characteristics of the collected surface electromyographic signals by using two characteristic parameters, adding the characteristics, analyzing data change characteristics, determining a fatigue coefficient value corresponding to the muscle fatigue of an individual according to a data change rule, and taking the fatigue coefficient value as a basis; when a testee is subjected to rehabilitation training, a large amount of sample data needs to be acquired, if the myoelectric signal in a certain effective time period is lower than the fatigue coefficient determined by an individual in advance through parameters extracted by WL and WSE, the fatigue state is judged, and the upper computer immediately reminds the rehabilitation training personnel to stop acquiring so as to ensure the reliability of acquired data and the safety of the patient. The characteristic parameters for evaluating the muscle fatigue degree use time domain waveform length and nonlinear wavelet singular entropy.
S3: collecting an online electromyographic signal;
the upper computer system integrates electromyographic signal acquisition, data preprocessing, feature extraction, data storage, waveform display and fatigue state reminding; in the rehabilitation process, the limb on the healthy side of a patient performs corresponding actions, the lower limb rehabilitation robot is actively controlled to drive the ankle joint of the limb on the affected side of the patient to move up and down, the tibialis anterior muscle is contracted when the ankle joint of the limb on the healthy side moves every time, at the moment, the disposable electrode plate is in contact with the skin of a human body, the electromyographic signals are transmitted to the portable electromyographic signal acquisition instrument, data are transmitted to the multifunctional upper computer programmed by Labview2020 and Matlab in a mixed mode through the Bluetooth receiver, and real-time storage, display, data processing and fatigue online evaluation of the electromyographic signals are achieved. The lower limb ankle joint pedal of the lower limb rehabilitation robot has an angular displacement range of (-7), the sole of a tested person is jointed with the rehabilitation robot pedal, and the pedal moves up and down to drive the sole of the tested person to move up and down, wherein the action mainly comprises rehabilitation training on the ankle joint; when a tested person carries out ankle joint rehabilitation training on the lower limb rehabilitation robot, the electromyographic signals are transmitted to a computer by a portable electromyographic signal acquisition instrument and are compared with the fatigue coefficient of the tibialis anterior muscle of the individual through characteristic parameter extraction to obtain the muscle fatigue degree evaluation result. The portable electromyographic signal acquisition instrument is connected with the two buttons of the disposable silver/silver chloride electrode, and the other side of the disposable silver/silver chloride electrode is connected with the skin of a human body through conductive adhesive.
The working principle is as follows:
aiming at the verification of the online muscle fatigue evaluation method of the tibialis anterior of the lower limbs, ankle joint single action which can cause muscle fatigue of a human body is selected, and the tibialis anterior with the strongest relevance degree with the action is analyzed to be used as a fatigue evaluation object. In order to reduce the influence of other factors during data acquisition, different testees are selected to perform the same action and the same muscle tissue for detection, and the surface of the acquired skin is subjected to alcohol treatment, so that the aim of cleaning is fulfilled. In the experiment, the subject is explained about the action key points and the attention items, so that the subject is ensured to receive the experiment under the normal state. In addition, three-dimensional animation display is carried out to eliminate incorrect influence of the testee caused by subjective fatigue. And selecting the time domain waveform length and the nonlinear wavelet singular entropy as fatigue coefficients for determining the individual muscle fatigue. The patient firstly carries out active training to control the rehabilitation robot to move, at the moment, the pedal of the right leg and the pedal of the left leg of the tested person are not moved, and the pedal of the left leg drive the ankle joint of the patient to move up and down. As shown in fig. 4, a is a right leg pedal, B is a pedal, C is a healthy lower limb, D is a damaged lower limb, the healthy lower limb performs electromyographic signal acquisition to actively control the left leg of the rehabilitation robot and perform rehabilitation training on the left limb of the patient, the left pedal B floats up and down within a range of-7 to 7 degrees, and then the electromyographic signal of the damaged limb D of the patient can be acquired to perform corresponding muscle fatigue online evaluation. As shown in fig. 3, for online evaluation of an upper computer interface, i is a waveform display area, 1 in i is a first channel waveform display, 2 is a second channel waveform display, ii is a serial port matching selection area of a portable electromyogram signal acquisition instrument, and iii is a sampling rate setting area; according to the invention, the sampling rate is 1024, IV is serial port serial numbers, each serial port corresponds to one serial number, the middle digital frame is set for the cycle times, but when the tested person does not reach the set times, the fatigue phenomenon occurs, namely the V-area green light highlight reminding occurs. As shown in fig. 1, an online fatigue evaluation schematic diagram is obtained by acquiring data through an upper computer by using a portable electromyographic signal acquisition instrument, preprocessing the data online, extracting features, extracting and superposing the two features of the wire, and determining a fatigue coefficient for an individual; when the online fatigue evaluation is carried out, the electromyographic signals of the tested person are subjected to data processing, the characteristic value obtained by characteristic extraction is compared with the determined fatigue coefficient, and whether the muscle is in the state or not can be judged. As shown in fig. 2, the graph of the online fatigue evaluation result of the tibialis anterior muscle of the subject was subjected to a series of data processing and analysis to obtain the inflection point shown by the mark point in the graph, i.e., X58 and Y1.238, and after that, the waveform was rapidly decreased to explain the fatigue phenomenon of the subject, and the fatigue coefficient for the individual was determined to be 1.238, and the fatigue coefficient was used as the reference standard in the online fatigue evaluation.
sEMG preprocessing uses both temporal and nonlinear types of eigenvalues.
The time domain characteristic value basic formula is as follows:
waveform length pair (WL):
wherein x is(i)Representing the motion voltage amplitude at the ith point of a certain motion. i is 1, 2, …, N is a time sample sequence of the myoelectric signal of length N.
The basic formula of the nonlinear characteristic value is as follows:
from the theoretical knowledge of singular values, the matrix can be decomposed as shown below:
Wm×n＝Um×l∧l×lVl×n
wherein: m is 2S，n＝N/2S,Λ＝diag(λ1，λ2，…λl) Is a diagonal matrix of singular values, the singular values satisfying lambda1≥λ2…≥λlWavelet singular entropy data processing combining wavelet transformation, singular value decomposition, information entropy for quantitative description of frequency and component characteristics of signalsThe method comprises the following steps:
wherein Δ piThe singular entropy of the increment wavelet of the order i is defined as:
in the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A wearable myoelectric method for evaluating muscle movement fatigue degree on line is characterized by comprising the following steps:
s1: carrying out ankle joint rehabilitation training on a tested person aiming at the lower limb rehabilitation robot, and preparing the recovery training and myoelectric signal acquisition of the tested person;
s2: evaluating muscle fatigue degree characteristic parameter selection and extraction;
s3: and collecting an online electromyographic signal.
2. The wearable electromyography method for online assessment of muscle motor fatigue of claim 1, wherein: the specific steps of S1 are as follows:
selecting a plurality of different healthy subjects, cleaning the collected parts with alcohol before collection, placing two sensors at the same tibialis anterior muscle and the rectus femoris of each subject, and having smaller impact on the tibialis rectus muscle when the ankle joint is single-acting, so that fatigue evaluation is not carried out, and the interference of external uncertain factors is reduced; when the testee carries out rehabilitation training, the animation in the upper computer is opened at the same time, the influence of subjective fatigue is eliminated, and then the testee is carried out rehabilitation training and electromyographic signal acquisition.
3. The wearable electromyography method for online assessment of muscle motor fatigue of claim 1, characterized in that: the specific steps of S2 are as follows:
respectively selecting Waveform Length (WL) from time domain characteristics, selecting Wavelet Singular Entropy (WSE) from nonlinear characteristics, extracting characteristics of the collected surface electromyographic signals by using two characteristic parameters, adding the characteristics, analyzing data change characteristics, determining a fatigue coefficient value corresponding to the muscle fatigue of an individual according to a data change rule, and taking the fatigue coefficient value as a basis; when a testee is subjected to rehabilitation training, a large amount of sample data needs to be acquired, if the myoelectric signal in a certain effective time period is lower than the fatigue coefficient determined by an individual in advance through parameters extracted by WL and WSE, the fatigue state is judged, and the upper computer immediately reminds the rehabilitation training personnel to stop acquiring so as to ensure the reliability of acquired data and the safety of the patient.
4. The wearable electromyography method for online assessment of muscle motor fatigue of claim 1, wherein: the specific steps of S3 are as follows:
the upper computer system integrates electromyographic signal acquisition, data preprocessing, feature extraction, data storage, waveform display and fatigue state reminding; in the rehabilitation process, the limb on the healthy side of a patient performs corresponding actions, the lower limb rehabilitation robot is actively controlled to drive the ankle joint of the limb on the affected side of the patient to move up and down, the tibialis anterior muscle is contracted when the ankle joint of the limb on the healthy side moves every time, at the moment, the disposable electrode plate is in contact with the skin of a human body, the electromyographic signals are transmitted to the portable electromyographic signal acquisition instrument, data are transmitted to the multifunctional upper computer programmed by Labview2020 and Matlab in a mixed mode through the Bluetooth receiver, and real-time storage, display, data processing and fatigue online evaluation of the electromyographic signals are achieved.
5. The wearable electromyography method for online assessment of muscle motor fatigue of claim 2, characterized in that: the healthy testers select different ages, different sexes, heights, weights and the like, and are used for verifying the stability of the wearable method for online muscle fatigue evaluation; when testing the muscle fatigue possibly caused by different production activities, the muscle tissue with the strongest relevance is selected, and the selection method is more convincing to the evaluation of the muscle fatigue state of the human body.
6. The wearable electromyography method for online assessment of muscle motor fatigue of claim 3, wherein: and evaluating the muscle fatigue degree characteristic parameters by using time domain waveform length and nonlinear wavelet singular entropy.
7. The wearable electromyography method for online assessment of muscle motor fatigue of claim 4, wherein: the lower limb ankle joint pedal of the lower limb rehabilitation robot has an angular displacement range of (-7 degrees), the sole of a tested person is attached to the rehabilitation robot pedal, the pedal moves up and down to drive the sole of the tested person to move up and down, and the action mainly comprises rehabilitation training on the ankle joint; when a tested person carries out ankle joint rehabilitation training on the lower limb rehabilitation robot, the electromyographic signals are transmitted to a computer by a portable electromyographic signal acquisition instrument and are compared with the fatigue coefficient of the tibialis anterior muscle of the individual through characteristic parameter extraction to obtain the muscle fatigue degree evaluation result.
8. The wearable electromyography method for online assessment of muscle motor fatigue of claim 4, characterized in that: the portable electromyographic signal acquisition instrument is connected with two buttons of a disposable silver/silver chloride electrode, and the other side of the disposable silver/silver chloride electrode is connected with the skin of a human body through a conductive adhesive.
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| CN116269393B (en) * | 2023-05-22 | 2023-08-01 | 南京航空航天大学 | Interactive fitness fatigue monitoring method and device integrating myoelectricity and subjective perception |
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