摘要
A hands-free method is proposed to control an electric powered wheelchair （EPW） based on surface electromyography （sEMG） signals. A CyberLink device is deployed to obtain and analyze forehead sEMG signals generated by the facial movements. The autoregressive （AR） model is used to extract sEMG features. Then, the back-propagation artificial neural network （BPANN） is proposed to recognize different facial movement patterns and improved by Bayesian regularization and Levenberg-Marquardt （LM） algorithm. A sEMG based human-machine interface （HMI） is designed to map facial movement patterns into corresponding control commands. The experimental results show that the method is simple, real-time and have a high recognition rate.
A hands-free method is proposed to control an electric powered wheelchair （EPW） based on surface electromyography （sEMG） signals. A CyberLink device is deployed to obtain and analyze forehead sEMG signals generated by the facial movements. The autoregressive （AR） model is used to extract sEMG features. Then, the back-propagation artificial neural network （BPANN） is proposed to recognize different facial movement patterns and improved by Bayesian regularization and Levenberg-Marquardt （LM） algorithm. A sEMG based human-machine interface （HMI） is designed to map facial movement patterns into corresponding control commands. The experimental results show that the method is simple, real-time and have a high recognition rate.
基金
supported by the International Cooperation Project of Ministry of Science and Technology(2010DFA12160)
