NDLI: EMG-based motion intention detection for control of a shoulder neuroprosthesis

Content Provider	IEEE Xplore Digital Library
Author	Kirsch, R.F. Au, A.T.C.
Copyright Year	1997
Description	Author affiliation: Case Western Reserve Univ., Cleveland, OH, USA (Kirsch, R.F.)
Abstract	A method for predicting shoulder and motions from electromyograms (EMGs) from shoulder muscles using a time-delayed artificial neural network (TDANN) is described. The chosen network was found to be capable of characterizing the nonlinear and dynamic relationship between the EMG signals recorded from 6 shoulder muscles and the resulting shoulder and elbow motions in 5 able-bodied subjects. Preliminary work in one individual with tetraplegia due to spinal cord injury indicate that the same TDANN structure (although with a different set of muscle EMGs) will be also be sufficient to detect these motions in this population. This ability to detect shoulder and elbow motions would allow neuroprostheses based on functional neuromuscular stimulation (FNS) to appropriately vary stimulation patterns in a very natural manner for different tasks.
Starting Page	1944
Ending Page	1945
File Size	252669
Page Count	2
File Format	PDF
ISBN	0780342623
ISSN	1094687X
DOI	10.1109/IEMBS.1997.758719
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion detection Motion control Shoulder Muscles Elbow Electromyography Spinal cord injury Artificial neural networks Neuromuscular stimulation Control systems
Content Type	Text
Resource Type	Article

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