Hybrid soft computing systems for electromyographic signals analysis: a review.
Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.
Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia. xiehb@sjtu.org.
Hong-Bo
HB
Xie
Tianruo
T
Guo
Siwei
S
Bai
Socrates
S
Dokos
D003205:Computing Methodologies; D004576:Electromyography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted