Xie Hong-Bo, Guo Tianruo, Bai Siwei, Dokos Socrates
Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2052, Australia.
Biomed Eng Online. 2014 Feb 3;13:8. doi: 10.1186/1475-925X-13-8.
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.
肌电图（EMG）是一种从人体骨骼肌采集的生物信号。EMG信号分析已被广泛用于检测人体运动意图、控制各种人机接口、诊断神经肌肉疾病以及对神经肌肉骨骼系统进行建模。随着人工智能和软计算的发展，已经提出了许多用于此目的的复杂技术。混合软计算系统（HSCS）是这些不同技术的集成，旨在进一步提高EMG分析的有效性、效率和准确性。本文综述并比较了用于EMG分析的神经网络、支持向量机、模糊逻辑、进化计算和群体智能的关键组合。我们还从基本软计算技术、这些技术的进一步组合及其在EMG分析中的其他应用方面，给出了关于HSCS在EMG分析中未来可能发展的建议。
