Hybrid soft computing systems for electromyographic signals analysis: a review

BioMedical Engineering OnLine

Hong-Bo Xie¹  Tianruo Guo¹  Siwei Bai¹  Socrates Dokos¹ 

[1] Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia

关键词: Neuromuscular disease diagnosis;    Modeling;    Pattern classification;    Hybrid soft computing system;    Electromyography;   

Others  :  797205
DOI  :  10.1186/1475-925X-13-8

 received in 2013-12-01, accepted in 2014-01-30,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Xie et al.; licensee BioMed Central Ltd.

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