Finger motion classification using surface-electromyogram signals

Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010

Volumn , Issue , 2010, Pages 37-42

  Ishikawa, Keisuke ^a   Toda, Masashi ^a   Sakurazawa, Shigeru ^a   Akita, Junichi ^b   Kondo, Kazuaki ^c   Nakamura, Yuichi ^c  

^a FUTURE UNIVERSITY HAKODATE   (Japan)

^b KANAZAWA UNIVERSITY   (Japan)

^c KYOTO UNIVERSITY   (Japan)

Author keywords

Finger motion classification; Support vector machines(SVM); Surface Electromyogram signals(EMG)

Indexed keywords

CEPSTRAL COEFFICIENTS; CLASSIFICATION TECHNIQUE; DEGREES OF FREEDOM; ELECTROMYOGRAM SIGNALS; EMG SIGNAL; FINGER MOTION; FINGER MOVEMENTS; INTEGRAL CALCULUS; SUPPORT VECTOR MACHINES(SVM); SURFACE EMG;

DATA HANDLING; FABRICS; GEARS; INFORMATION SCIENCE; INTEGRAL EQUATIONS; MECHANICS; MUSCLE;

SUPPORT VECTOR MACHINES;

EID: 78649278893     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIS.2010.131     Document Type: Conference Paper

References (8)

1. Barry DT, Gordon KE, Hinton GG. Acoustic and surface EMG diagnosis of pediatric muscle disease. Muscle Nerve 1990;13:286.290.
2. Abel EW, Zacharia PC, Farrow A, Farrow TL. Neural network analysis of the EMG interference pattern. Med Eng Phys 1996;18(1):19.27.
3. T. Furey, N. Cristianini, N. Duffy, D. Bednarski, M. Schummer, and D. Haussler, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, vol. 16, pp. 906.914, 2000.
4. S. Tong and E. Chang. Support vector machine active learning for image retrieval. ACM International Conference on Multimedia, pages 107.118, October 2001.
5. T. Joachima. Text Categorization with Support Vector Machines: Learing with Many relevant Features. In European Conference on Machine Learning (ECML), 1998.
6. Masahiro YOSHIKAWA, Masahiko MIKAWA, Kazuyo TANAKA, Real-Time Hand Motion Classification Using EMG Signals with Support Vector Machines, The IEICE transactions on information and systems, J92-D(1), 93-103, 2009.
7. Toru SHINMURA. Junichi AKITA, Shigeru SAKURAZAWA, Masashi Toda. Presice Multi-channel Electromyography Measurement System Using Shield and Power Supply Function of Conductive Wear. Transactions of Information Processing Society of Japan, 48(12), 3784-3792,2007.
8. Chih-Chung Chang, Chih-Jen Lin, LIBSVM - A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/ cjlin/libsvm/