[Pattern recognition of surface electromyogram based on multi-scale principal component analysis].
Multi-scale principal component analysis based on wavelet transform was applied in feature extraction of sEMG, and bayes classifier was used for pattern classification in this paper. The experiment showed that when Harr wavelet or bior2.6 wavelet was employed to decompose EMG at 5 levels, this method resulted in good performance in the pattern recognition of six movements including varus, ectropion, hand grasps, hand extension, upwards flexion and downwards flexion, with the accuracy of 99.44%. It was superior to the feature extraction based on the statistic feature of wavelet coefficients combined with dimension-reduce by PCA. The research indicated that the proposed method can successfully identify many kinds of movements.
Tian
Xi-Ying
XY
Institute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240.
Lei
Min
M
D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted