Surface myoelectric signal classification for prostheses control

Journal of Medical Engineering and Technology

Volumn 29, Issue 5, 2005, Pages 203-207

  Al Assaf, Y ^a   Al Nashash, H ^a  

^a AMERICAN UNIVERSITY OF SHARJAH   (United Arab Emirates)

Author keywords

EMG; Myoelectric Signal (MES); Neural Networks; PCA; Wavelets

Indexed keywords

ELECTROMYOGRAPHY; FEATURE EXTRACTION; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING; WAVELET TRANSFORMS;

MULTIRESOLUTION WAVELET ANALYSIS; MYOELECTRIC SIGNALS (MES); SIGNAL SEGMENTATION; TIME FREQUENCY ANALYSIS;

MYOELECTRICALLY CONTROLLED PROSTHETICS;

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; AVERAGING; BICEPS BRACHII MUSCLE; CLASSIFICATION; CLINICAL ARTICLE; ELBOW; EXTRACTION; FEMALE; HUMAN; MALE; MATHEMATICAL ANALYSIS; MOVEMENT (PHYSIOLOGY); MYOELECTRIC CONTROL; OPTICAL RESOLUTION; PRINCIPAL COMPONENT ANALYSIS; PROSTHESIS; SURFACE MYOELECTRIC SIGNAL; TIME; TRICEPS BRACHII MUSCLE; WRIST;

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; ELBOW JOINT; ELECTROMYOGRAPHY; FEMALE; HUMANS; JOINT PROSTHESIS; MALE; MOVEMENT; MUSCLE CONTRACTION; MUSCLE, SKELETAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; WRIST JOINT;

EID: 25144523374     PISSN: 03091902     EISSN: None     Source Type: Journal    
DOI: 10.1080/03091900412331289906     Document Type: Article

References (18)

1. Hudgins, B., Englehart, K., Parker, P., and Scott, R., 1997, A microprocessor based multifunction Myoelectric control system. Proceedings of the 23rd Canadian Medical and Biological Engineering Society Conference, Toronto, May.
2. Kumaravel, N., and Kavitha, V., 1994, Automatic diagnosis of neuromuscular diseases using neural network. Biomedical Sciences Instrumentation, 30, 245-250.
3. Scott, R., and Parker, P., 1988, Myoelectric prostheses: State of the art. Journal of Medical Engineering & Technology, 12, 143-151.
4. Englehart, K., and Hudgins, B., 2003, A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50, 848-854.
5. Hudgins, B., Parker, P., and Scott, R., 1993, A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 40, 82-94.
6. Katutoshi, K., Koji, O., and Takao, T., 1992, A discrimination system using neural networks for EMG-controlled prostheses. Proceedings of IEEE Int. Workshop Robot Human Communications (IEEE), Tokyo, Japan, 69-74.
7. Englehart, K., Hudgins, B., and Parker, P., 2001, A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomedical Engineering, 48, 302-311.
8. Englehart, K., Hudgins, B., Parker, P., and Stevenson, M., 1999, Classification of the myoelectric signal using time-frequency based representations. Medical Engineering and Physics, 21, 431-438.
9. Kelly, M., Parker, P., and Scott, R., 1990, The application of neural networks to myoelectric signal analysis: a preliminary study. IEEE Trans Biomedical Engineering, 37, 221-230.
10. Kuruganti, U., Hudgins, B., and Scott, R., 1995, Two-channel enhancement of a multifunction control system. IEEE Transactions on Biomedical Engineering, 42, 109-111.
11. Zalzala, A., and Chiyaratana, N., 2000, Myoelectric signal classification using evolutionary hybrid RBF-MLP networks. Proceedings of the 2000 Congress on Evolutionary Computation, 691-698. San Diego, CA, 2.
12. Haykin, S., 1994, Neural Networks: A Comprehensive Foundation (New York: Macmillan).
13. Halici, U., Erol, A., and Ongun, G., 1999, Industrial applications of hierarchical neural networks: Character recognition and finger print classification. In Industrial Applications of Neural Networks, International Series on Computational Intelligence. Edited by L.C. Jain and V.R. Vemury, CRC Press, USA, pp. 159-192.
14. Qian, S., and Chen, D., 1996, Joint time-frequency analysis: Methods and Applications (Upper Saddle River, N.J.: Prentice Hall).
15. Mallat, S., and Zhong, S., 1992, Characterization of signals for multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 710-732.
16. Donoho, D., Huo, X., and Yu, T., 1999, Wavelab Soft-ware, Stanford University, http://www-stat.stanford.edu/~wavelab.
17. Chaiyaratana, N., Zalzala, A., and Datta, D., 1996, Myoelectric signals pattern recognition for intelligent functional operation of upper-limb prosthesis. Proceedings of the 1st European Conference on Disability, Virtual Reality & Associated Technologies, ECOVRAT and University of Reading, UK, 151-160.
18. Neurosolution software, 2000, Neural Network software, NeuroDimension, Inc, http://www.nd.com.