Gait pattern recognition of KPCAGASVM based on Mechanomyography(MMG)
Exoskeleton robots are developing rapidly,and the recognition of motor intent based on biological signals has been valued in human-machine collaborative control.To solve the disadvantages that EMG signal is susceptible to muscle fatigue and the acquisition requirements are high,a pattern recognition scheme based on Kernel Principal Component Analysis(KPCA)and improved Support Vector Machine(SVM)based on mechanomyography(MMG)signal is proposed to study five kinds of gait pattern recognition,including flat walking,going upstairs,going downstairs,going uphill and downhill.Based on the parameters tune of the GA algorithm,the recognition accuracy of KPCAGASVM is 97.33％,which is better than that of PCAGASVM and other classifiers Experiment results show that KPCA-SVM based on MMG signals is an effective gait rec-ognition scheme.
exoskeletonMechanomyography(MMG)Genetic Algorithm(GA)Support Vector Machine(SVM)Kernel Principal Component Analysis(KPCA)
万方数据
维普
