Muscle fatigue detection method based on feature extraction and deep learning
Author:
Affiliation:
1. Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China/P. R. China,230031
2. Air Force Medical Center, PLA,Aeronautical Physiological Identification Training Laboratory,Beijing,China/P. R. China,100142
Funder
Natural Science Foundation of Anhui Province
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9778230/9778231/09778406.pdf?arnumber=9778406
Reference16 articles.
1. Muscle fatigue state classification system based on surface electromyography signal;cao;Journal of Computer Applications,2018
2. Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning
3. Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM
4. Detection of Muscle Fatigue by Fusion of Agonist and Synergistic Muscle sEMG Signals
5. Surface EMG based muscle fatigue evaluation in biomechanics
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System;Sensors;2024-04-08
2. Exoskeleton Recognition of Human Movement Intent Based on Surface Electromyographic Signals: Review;IEEE Access;2024
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