NDLI: Performance of electromyography recorded using textile electrodes in classifying arm movements

Content Provider	IEEE Xplore Digital Library
Author	Guanglin Li Yanjuan Geng Dandan Tao Ping Zhou
Copyright Year	2011
Description	Author affiliation: Rehabilitation Institute at Chicago, Chicago, IL, USA (Ping Zhou) \|\| Key Laboratory of Health Informatics of Chinese Academy of Sciences (CAS), Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, Guangdong 518055 China (Guanglin Li; Yanjuan Geng) \|\| Harbin institute of technology, Shenzhen graduate school, and the Key Laboratory of Health Informatics of Chinese Academy of Sciences (CAS), Shenzhen Institutes of Advanced Technology, Shenzhen Guangdong 518055, China (Dandan Tao)
Abstract	Electromyography (EMG) signals are commonly recorded using the Ag/AgCl gel electrodes in myoelectric prosthetic control. While a gelled electrode may provide high-quality EMG recordings, it is inconvenient in clinical application of a myoelectric prosthesis. A novel type of signal sensors-textile electrodes should be ideal in control of myoelectric prostheses. However, it is unknown whether the performance of textile electrodes is comparable to commonly used electrodes in classifying arm movements. In this study, the custom-made bipolar textile electrodes were fabricated using copper-based nickel-plated conductive fabric and were used to record EMG signals. The performance of EMG signals recorded with textile electrodes in identifying nine arm and hand movements were investigated. Our pilot results showed that the average classification accuracy across six able-bodied subjects was 94.05% when using textile electrodes and 94.26% when using conventional electrodes, with no significant difference between the two types of electrodes (p=0.81). The pilot results suggest that the textile electrodes could achieve similar performance in classifying arm movements in control of myoelectric prostheses as the gelled metal electrodes.
Starting Page	4243
Ending Page	4246
File Size	569523
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091053
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Electromyography Textiles Accuracy Metals Muscles Prosthetics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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