Open AccessAssessment of Muscles Fatigue Based on Surface EMG Signals Using Machine Learning and Statistical Approaches: A Review
Author(s) -
Hayder A. Yousif,
Ammar Zakaria,
Norasmadi Abdul Rahim,
Ahmad Faizal Salleh,
Mustafa F. Mahmood,
Khudhur A. Alfarhan,
Latifah Munirah Kamarudin,
Syed Muhammad Mamduh Syed Zakaria,
Ali Majid Hasan,
Moaid K. Hussain
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/705/1/012010
Subject(s) - electromyography , muscle fatigue , signal (programming language) , frequency domain , contraction (grammar) , time domain , muscle contraction , signal processing , computer science , physical medicine and rehabilitation , biomedical engineering , medicine , anatomy , computer vision , telecommunications , radar , programming language
Muscle fatigue is described by the decline in muscle maximum force during contraction. The fatigue occurs in the nervous or muscle fibre cells. The nerves produce a high-frequency signal to gain the maximum contraction, but it cannot sustain the high frequency signal for a long time, and that leads to a decline in muscle force. The surface Electromyography (EMG) is the dominant method to detect muscle fatigue because the EMG signals give more information about the muscle’s activities. This review discussed the EMG signal processing and the methods of detection muscles fatigue with three domains (time domain, frequency domain, and time-frequency domain) based on EMG signals that are collected from the muscles during dynamic and static movements.