An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often...An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accu- rately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.展开更多
The EMG signal is a present field of research which is a driving force in sources of rehabilitating robots. The FFT with Kaiser Window was used in this paper to analyze the spectral characteristics of the EMG signal a...The EMG signal is a present field of research which is a driving force in sources of rehabilitating robots. The FFT with Kaiser Window was used in this paper to analyze the spectral characteristics of the EMG signal according to the characteristic of time changing and nonlinearity for the EMG signal and good results have been obtained. The singular value expressing the property of every EMG signal at each channel was taken out. It offered important data for the actual control of rehabilitating robots.展开更多
The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The featu...The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The features were used to develop a terrain identification method. The technology can be used to develop an intelligent transfemoral prosthetic limb with terrain identification capability, The EMG signals from 8 hip muscles of 13 healthy persons were recorded as they walked on the different terrains. The signals from the sound side of a transfemoral amputee were also recorded. The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type. The procedure was simplified by using only the signals from three muscles. The identification process worked well in an intelligent prosthetic knee in a laboratory setting.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60171006) and the National Basic Research Program (973) of China (No. 2005CB724303)
文摘An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accu- rately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.
文摘The EMG signal is a present field of research which is a driving force in sources of rehabilitating robots. The FFT with Kaiser Window was used in this paper to analyze the spectral characteristics of the EMG signal according to the characteristic of time changing and nonlinearity for the EMG signal and good results have been obtained. The singular value expressing the property of every EMG signal at each channel was taken out. It offered important data for the actual control of rehabilitating robots.
基金Supported by the National Natural Science Foundation of China (No30170242) and the National High-Tech Research and Developmen(863) Program (No. 2001AA320601) of China
文摘The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The features were used to develop a terrain identification method. The technology can be used to develop an intelligent transfemoral prosthetic limb with terrain identification capability, The EMG signals from 8 hip muscles of 13 healthy persons were recorded as they walked on the different terrains. The signals from the sound side of a transfemoral amputee were also recorded. The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type. The procedure was simplified by using only the signals from three muscles. The identification process worked well in an intelligent prosthetic knee in a laboratory setting.
