NDLI: EMG pattern recognition control of multifunctional prostheses by transradial amputees

Content Provider	IEEE Xplore Digital Library
Author	Guanglin Li Kuiken, T.A.
Copyright Year	2009
Description	Author affiliation: Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago and Department of Physical Medicine and Rehabilitation, Northwestern University, IL 60611 USA. He is now with Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518067, China (Guanglin Li) \|\| Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago and Department of Physical Medicine and Rehabilitation, Northwestern University, IL 60611 USA (Kuiken, T.A.)
Abstract	Electromyogram (EMG) pattern recognition approach has been investigated widely with able-bodied subjects for control of multifunctional prostheses and verified with high performance in identifying different movements. However, it remains unclear whether transradial amputees can achieve similar performance. In this study, we investigated the performance of EMG pattern recognition control of multifunctional transradial prostheses in five subjects with unilateral below-elbow amputation. Testing results on both residual and intact arms showed that the average classification error (21%) of amputated arms for ten motion classes (four wrist movements, six hand grasps) and a ‘no movement’ class over all five subjects was about 15% higher than that of intact arms. For six basic motion classes (wrist flexion/extension, wrist pronation/supination, and hand open/close), the average classification error over all five subjects was about 7% from residual arms, which was similar to the result from intact arms (6%). Only six optimal electrode channels might be needed to provide an excellent myoelectric control system for the six basic movements. These results suggest that the muscles in the residual forearm may produce sufficient myoelectric information to allow the six basic motion control, but insufficient information for more hand functions with fine finger movements.
Starting Page	6914
Ending Page	6917
File Size	488449
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333628
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electromyography Pattern recognition Prosthetics Arm Wrist Testing Electrodes Optimal control Control systems Muscles
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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