Neural networks for online classification of hand and finger movements using surface EMG signals

8th Seminar on Neural Network Applications in Electrical Engineering, Neurel-2006 Proceedings

Volumn , Issue , 2006, Pages 167-171

  Tsenov, G ^a   Zeghbib, A H ^b   Palis, F ^b   Shoylev, N ^c   Mladenov, V ^a  

^a TECHNICAL UNIVERSITY OF SOFIA   (Bulgaria)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

^c UNIVERSITY OF CHEMICAL TECHNOLOGY AND METALLURGY   (Bulgaria)

Author keywords

EMG signals; Feature extraction; Hand and finger movements identification; Neural networks

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMEDICAL ENGINEERING; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; FUNCTION EVALUATION; IMAGE CLASSIFICATION; MUSCLE; MYOELECTRICALLY CONTROLLED PROSTHETICS; PROSTHETICS; VEGETATION;

(1 1 0) SURFACE; FINGER MOVEMENTS; IDENTIFICATION PROCEDURES; MYOELECTRIC PROSTHESES; MYOELECTRIC SIGNALS (MES); NEURAL NETWORK APPLICATIONS; ON LINE ALGORITHMS; SURFACE EMG SIGNALS; TRAINED NEURAL NETWORKS;

NEURAL NETWORKS;

EID: 46749085201     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NEUREL.2006.341203     Document Type: Conference Paper

References (12)

1. Chang G-C, Kang W-J, Luh J-J, Cheng C-K, Lai J-S, Chen J-JJ, Kuo T-S, "Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface". Med Eng Phys. 1996 Oct;18(7):529-37. PMID: 8892237 [PubMed -indexed for MEDLINE].
2. Hefftner G, Jaros G, "The electromyogram (EMG) as a control signal for functional neuromuscular, Stimulation, part I: Autoregressive modeling as a mean of EMG signature discrimination". IEEE Trans Biomed Eng 1988, 35(6), pp. 228-35.
3. Zardoshti-Kermani M, Wheeler BC, Badie K, Hashemi RM, "EMG feature evaluation for movement, control of upper extremity prostheses". IEEE Trans Rehabil Eng 1995;Vol.3, No.4, pp. 324-333.
4. C. Bonivento, A. Davalli, C. Fantuzzi, R. Sacchetti, S. Terenzi, "Automatic tuning of myoelectric prostheses" Journal of Rehabilitation Research and Development Vol. 35 No. 3, July 1998, pp. 294-304.
5. K.Englehart, B. Hudgin, and P. A. Parker, "A wevelet based continuous classification scheme for multifunction myoelectric control", IEEE Transactions on Biomedical Engineering, vol. 48, no. 3, pp. 302-311, March 2001.
6. Z. K. Mahyar, W. C. Bruce, B. Kambiz, and M. II. Reza, EMG feature evaluation for movement control of Upper extremity prostheses", IEEE Transactions on Rehabilitation Engineering, Dec 1995.
7. B. Hudgins, P. Parker and R. N. Scott, "New strategy for multi-function myoelectric control "IEEE Transactions on Biomedical Engineering, vol. 40, no. 1, pp. 82-94, Jan. 1993.
8. S. Haykin, Neural Networks: A Comprehensive Foundation, (2nd Edition 1998), ISBN: 0132733501. Prentice Hall, 1998.
9. T. Kohonen. Self organization and associate memory. Springer-Verlag, London, 3rd edition, 1989.
10. T. Kohoenen. Improved versions of learning vector quantization. In International Joint conference on Neural Networks. Vol:1, p:545-550, San Diego. 1990.
11. A.H. Zeghbib, F. Palis, G. Tsenov, N. Shoylev, V. Mladenov, "Performance of surface EMG signals identification using intelligent computational methods", WSEAS Transactions on Systems, Issue7, Volume 4, July 2005 p.1118-1125.
12. A.H. Zeghbib, F. Palis, G. Tsenov, N. Shoylev, V. Mladenov, "Fuzzy systems and Neural Networks methods to identify hand and finger movements using surface EMG signals", 9-th WSEAS CSCC Multiconference Vouliagami, Athens, Greece, July 2005