A comprehensive review: electromyography signal analysis and classification methods for robotic rehabilitation

The examination of electromyography (EMG), including surface (sEMG) and intramuscular (iEMG) signals is essential for interpreting neuromuscular behaviour within diagnostic, therapeutic, and prosthetic limb control contexts. This survey explores recent research concerning comprehensive EMG signal analysis across its core phases, spanning initial signal capture and conditioning through feature derivation, probability density function evaluation, and classification. The survey emphasizes that combining conventional approaches with deep learning (DL) strategies has markedly improved classification performance, gesture identification, and assistance for rehabilitation uses and muscle fatigue assessment. It further demonstrates the expanding influence of DL instruments across multiple signal processing phases. Using a comparative assessment of more than thirty research works published between 2020 and 2025, the article particularly underscores the critical importance of intelligent rehabilitation robots, which have achieved elevated integration precision in motion encoding and system response.

Text 125436.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) Official URL or Download Paper: https://www.iieta.org/journals/jesa/paper/10.18280...

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