The application of wavelet transform and neural network to surface electromyographic signals for pattern recognition

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 7 VOLS, Issue , 2005, Pages 5009-5012

  Jianguo, Cui ^a,b   Xu, Wang ^a  

^a NORTHEASTERN UNIVERSITY   (China)

^b SHENYANG AEROSPACE UNIVERSITY   (China)

Author keywords

Neural network; Pattern recognition; Surface electromyographic (SEMG); Wavelet transform

Indexed keywords

BIOELECTRIC PHENOMENA; EIGENVALUES AND EIGENFUNCTIONS; MICROELECTRODES; MUSCULOSKELETAL SYSTEM; NEURAL NETWORKS; NEUROLOGY; PATTERN RECOGNITION; SKIN; WAVELET TRANSFORMS;

BIOELECTRIC SIGNALS; IDENTIFICATION RATES; MOVEMENT PATTERNS; SURFACE ELECTROMYOGRAPHIC (SEMG); WRIST CIRCUMROTATES;

ELECTROMYOGRAPHY;

EID: 33846900674     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Balestra C, Frassinelli S, et al. Time-frequency analysis of surface myoelectric signals during athletic movement[J]. IEEE Engineering in Medicine and Biology Magazine,2001,20(6):106-115.
2. K. Yana et al. Surface electromyogram recruitment analysis using highter order spectrum. IEEE-EMBC and CMBEC. 1995,1345-1346.
3. Zhizeng Luo. Guangying Yang. The AR parameter model analyze method of surface electromyography. Sensor Technique Journal. 2003,(4):384-387.
4. Liyu Cai et al. A Nonlinear Analysis of Surface EMG signals Based on Chaos and Fractal Theory. Beijing Biomedical Engineering Journal. 2000,19 (2):82-86.
5. Haihong Zhang et al. A Pi-Sigma Neural Network Based electromyograph Signal Identification Method. Chinese Journal of Rehabilitation Medicine. 2000,15(1):34-37.