DocumentCode :
337185
Title :
EMG-based motion intention detection for control of a shoulder neuroprosthesis
Author :
Kirsch, R.F. ; Au, A.T.C.
Author_Institution :
Case Western Reserve Univ., Cleveland, OH, USA
Volume :
5
fYear :
1997
fDate :
1997
Firstpage :
1944
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
Keywords :
backpropagation; electromyography; feedforward neural nets; medical signal processing; motion estimation; neuromuscular stimulation; prosthetics; EMG-based motion intention detection; backpropagation; dynamic relationship; elbow motions; feedforward ANN; functional neuromuscular stimulation; motion prediction; nonlinear relationship; paralysed antagonists; shoulder muscle EMG; shoulder neuroprosthesis control; spinal cord injury; tetraplegia; time-delayed ANN; varied stimulation patterns; voluntary antagonists; Artificial neural networks; Control systems; Elbow; Electromyography; Motion control; Motion detection; Muscles; Neuromuscular stimulation; Shoulder; Spinal cord injury;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
ISSN :
1094-687X
Print_ISBN :
0-7803-4262-3
Type :
conf
DOI :
10.1109/IEMBS.1997.758719
Filename :
758719
Link To Document :
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