Method for judging hand fatigue based on surface electromyogram signals

A technology of myoelectric signal and myoelectric signal collection, applied in the field of hand fatigue judgment based on surface electromyographic signal, can solve the problems of unfavorable health, slow recovery, irreversible recovery, etc., achieve high network operation efficiency, improve expression ability, The effect of consuming less computing power

Pending Publication Date: 2021-07-16
NORTHWESTERN POLYTECHNICAL UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But when the keyboard and mouse are used for a long time, it will cause pain in the wrist and finger joints, and become the so-called "mouse hand".
These chronic strain injuries are not conducive to health, but also affect daily life and office efficiency, and the recovery is very slow or even irreversible

Method used

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  • Method for judging hand fatigue based on surface electromyogram signals
  • Method for judging hand fatigue based on surface electromyogram signals
  • Method for judging hand fatigue based on surface electromyogram signals

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Experimental program
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Embodiment Construction

[0036] The method for judging hand measurement based on surface electromyography signals provided by the present invention mainly includes the following steps:

[0037] (1) Establish a surface electromyographic signal data set for training and testing, read and process the collected data, use a one-dimensional convolutional network to build a training network model, put the training set into neural network training, and use the optimal model Real-time prediction of the currently collected EMG signals.

[0038] The implementation of this method further optimizes the above basic scheme:

[0039] (1) Modify the activation function from ReLu to tanh:

[0040] ReLu function expression:

[0041] f(x)=max(0,x)

[0042] tanh function expression:

[0043]

[0044] Comparing the two activation functions, it can be obtained that the gradient changes faster, that is, the convergence speed is faster during the training process, which can improve the efficiency of multi-classificatio...

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Abstract

The invention discloses a method for judging hand fatigue based on surface electromyogram signals. The method comprises the following steps: firstly, establishing a hand surface electromyogram signal data set for training and testing; secondly, marking normal, mild and severe states by using subjective feeling and spectral analysis, and dividing the states into training samples and test samples; thirdly, inputting a training sample with a label into the established network for training; and fourthly, obtaining a prediction result of a hand fatigue signal. According to the method, the human hand muscle fatigue state can be detected and accurately predicted through the network model, and a reference basis is provided for hand disease prevention and exercise training.

Description

technical field [0001] The invention belongs to the field of signal detection and sports medicine, and specifically designs a method for judging hand fatigue based on surface electromyography signals. Background technique [0002] In contemporary society, many people choose to use computers to help them complete their daily life and study. But when using the keyboard and mouse for a long time, it will cause soreness in the wrist and finger joints, and become the so-called "mouse hand". These chronic strain injuries are not conducive to health, but also affect daily life and office efficiency, and the recovery is very slow or even irreversible. Therefore, its prevention needs to be highly valued. [0003] Surface electromyography (sEMG) is a one-dimensional time series non-stationary bioelectric signal that reflects the activity of the neuromuscular system recorded from the muscle surface through electrodes, and can reflect the functional state of nerves and muscles. Effec...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/389A61B5/397G06K9/62G06N3/04G06N3/08
CPCA61B5/7264G06N3/08G06N3/048G06N3/045G06F18/2431G06F18/214
Inventor 宋端霄李雪晴刘梦瑶王晓岩张子凡高天德
Owner NORTHWESTERN POLYTECHNICAL UNIV
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