Presentation	2001/7/6 Simplification of EMG Feature Extraction System Using Backpropagation Learning Algorithm with Forgetting Hisashi SAKAKIHARA,
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Abstract(in English)	This paper reports a forearm motion discrimination system for control of myoelectric hand prostheses. The neural network used in the system can learn a mapping from the EMG spectral features. Simplification of EMG feature extraction system is important to design small-sized prosthesis control system. The EMG feature extraction circuit has 8 channel band-pass filters. The discriminating rates of the network trained by the backpropagation learning algorithm with forgetting are useful to estimate the feature extraction ability of band-pass filters. After drastic simplification of feature extraction circuits, the discriminating rate about 92% was carried out.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	EMG / hand prosthesis / feature extraction system / learning algorithm with forgetting
Paper #	MBE2001-45
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Conference Information
Committee	MBE
Conference Date	2001/7/6(1days)
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Paper Information
Registration To	ME and Bio Cybernetics (MBE)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Simplification of EMG Feature Extraction System Using Backpropagation Learning Algorithm with Forgetting
Sub Title (in English)
Keyword(1)	EMG
Keyword(2)	hand prosthesis
Keyword(3)	feature extraction system
Keyword(4)	learning algorithm with forgetting
1st Author's Name	Hisashi SAKAKIHARA
1st Author's Affiliation	Niihama National College of Technology()
Date	2001/7/6
Paper #	MBE2001-45
Volume (vol)	vol.101
Number (no)	181
Page	pp.pp.-
#Pages	7
Date of Issue