DocumentCode :
3506613
Title :
Wrist EMG signals identification using neural network
Author :
Oyama, Tadahiro ; Mitsukura, Yasue ; Karungaru, Stephen Githinji ; Tsuge, Satoru ; Fukumi, Minoru
Author_Institution :
Grad. Sch. of Adv. Technol. & Sci., Univ. of Tokushima, Tokushima, Japan
fYear :
2009
fDate :
3-5 Nov. 2009
Firstpage :
4286
Lastpage :
4290
Abstract :
Recently, researches of artificial arms and pointing devices using electromyogram (EMG) have been actively done. However, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist motions using EMG which was measured from the wrist, the range of application will extend furthermore. Moreover, it is predicted that convenience in putting on and taking off the electrode improves. Therefore, we focus on EMG measured from the wrist. In this paper, we aim the construction of wrist EMG recognition system by using fast statistical method and neural network.
Keywords :
biomechanics; biomedical electrodes; electromyography; feature extraction; medical signal processing; motion measurement; neural nets; pattern recognition; EMG; electrode; electromyogram; neural network; signal identification; statistical method; wrist; Arm; Electrodes; Electromyography; Feature extraction; Hidden Markov models; Linear discriminant analysis; Neural networks; Optical fiber devices; Signal processing; Wrist;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics, 2009. IECON '09. 35th Annual Conference of IEEE
Conference_Location :
Porto
ISSN :
1553-572X
Print_ISBN :
978-1-4244-4648-3
Electronic_ISBN :
1553-572X
Type :
conf
DOI :
10.1109/IECON.2009.5415065
Filename :
5415065
Link To Document :
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