Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification. (April 2018)

• Record Type: Journal Article
• Authors: Samuel, Oluwarotimi Williams; Zhou, Hui; Li, Xiangxin; Wang, Hui; Zhang, Haoshi; Sangaiah, Arun Kumar; Li, Guanglin
• Is Part Of: Computers & electrical engineering. Volume 67(2018)
• Page Start: 646
• Page End: 655
• Publication Date: 2018-04
• Subjects: Rehabilitation robotics -- Electromyography -- Time domain features -- Myoelectric prostheses -- Pattern recognition -- Upper-limb amputees; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.04.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: This study proposed three new time domain (TD) features for EMG Pattern recognition (EMG-PR). The performances of these features were examined using four performance metrics. The results showed that the newly proposed features outperform the previously used TD features. These features might improve the control performance of EMG-PR based prostheses. Abstract: Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based prostheses control method. Time-domain features have been shown to have good performance in upper limb movement classification. However, the performance of EMG-PR prostheses driven by the existing time-domain features is still unsatisfactory. Hence, this study proposed three new time-domain features to improve the performance of EMG-PR based strategy in arm movement classification. EMG signals were recorded from the residual arms of eight amputees while performing different upper limb movements. Then, the newly proposed features were extracted and used to classify their limb movements. Experimental results showed that the proposed features could achieved an average classification accuracy of 92.00% ± 3.11% which was 6.49% higher than that of the commonly used time-domain features ( p < 0.05). With three additional metrics, the proposed features also performed better, which suggest that the new features may be potential for improving the clinical performance of EMG-PR prostheses.