Because the single channel surface electromyographic （sEMG） signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in t...Because the single channel surface electromyographic （sEMG） signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in this paper. A distributed parallelly decision fusion algorithm fused classification results of the two signals to form a final judgment. After sEMG was decomposed by wavelet packet, feature information of some subspace was weaken, because subspace dimension was very large. To solve the problem, the paper proposed an improved wavelet packet decomposition algorithm, which extracted sample entropy from four subspaces of improved wavelet packet decomposition and took it as the feature information. Experimental results show that the intelligent wheelchair system based on sEMG and head gesture has not only a simple operation and shorter operating time, but also a better stability and security.展开更多
An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface(BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelc...An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface(BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors(start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis(especially those with aphasia) who can learn to pronounce only a single sound(e.g., ‘ah').展开更多
According to the decline of recognition rate of speech recognition system in the noise environments, an improved perceptually non-uniform spectral compression feature extraction algorithm is put forward in this paper....According to the decline of recognition rate of speech recognition system in the noise environments, an improved perceptually non-uniform spectral compression feature extraction algorithm is put forward in this paper. This method can realize an effective compression of the speech signals and make the training and recognition environments more matching, so the recognition rate can be improved in the noise environments. By experimenting on the intelligent wheelchair platform, the result shows that the algorithm can effectively enhance the robustness of speech recognition, and ensure the recognition rate in the noise environments.展开更多
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 generate...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.展开更多
基金supported by the International Cooperation Project of Ministry of Science and Technology (2010DFA12160)
文摘Because the single channel surface electromyographic （sEMG） signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in this paper. A distributed parallelly decision fusion algorithm fused classification results of the two signals to form a final judgment. After sEMG was decomposed by wavelet packet, feature information of some subspace was weaken, because subspace dimension was very large. To solve the problem, the paper proposed an improved wavelet packet decomposition algorithm, which extracted sample entropy from four subspaces of improved wavelet packet decomposition and took it as the feature information. Experimental results show that the intelligent wheelchair system based on sEMG and head gesture has not only a simple operation and shorter operating time, but also a better stability and security.
基金Project supported by the National High-Tech R&D Program(863)of China(No.2012AA011601)the National Natural Science Foundation of China(No.91120305)+3 种基金the University High Level Talent Program of Guangdong,China(No.N9120140A)the Foundation and Theoretical Science Project supported by Jiangmen Research Program(No.2014(17)the Fundamental Research Funds for the Central Universities,South China University of Technology(No.2014ZB0031)the Science Foundation for Young Teachers of Wuyi University(No.2013zk08)
文摘An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface(BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors(start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis(especially those with aphasia) who can learn to pronounce only a single sound(e.g., ‘ah').
基金supported by the International Science and Technology Cooperation Program of China (2010DFA12160)the National Natural Science Foundation of China (51075420),the National Natural Science Foundation of China (60905066)the Science & Technology Research Project of Chongqing CSTC(2010AA2055)
文摘According to the decline of recognition rate of speech recognition system in the noise environments, an improved perceptually non-uniform spectral compression feature extraction algorithm is put forward in this paper. This method can realize an effective compression of the speech signals and make the training and recognition environments more matching, so the recognition rate can be improved in the noise environments. By experimenting on the intelligent wheelchair platform, the result shows that the algorithm can effectively enhance the robustness of speech recognition, and ensure the recognition rate in the noise environments.
基金supported by the International Cooperation Project of Ministry of Science and Technology(2010DFA12160)
文摘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.
