Enhancing electromyography based locomotion mode classification when using powered lower limb prostheses
University of New Brunswick
The recent introduction of powered leg prostheses enables users to perform many more tasks than passive legs, such as ascending/descending stairs and ramps with greater ease, and standing from a sitting position. With this increased function comes a need for improved control that may be obtained from neural information recorded as the surface electromyographic (EMG) signal. The EMG signal has been shown to be a promising source of autonomous information to characterize the instantaneous mode of locomotion (level walking, ramp ascent/decent, stairs ascent/decent). This is of great value in that the prosthesis can alter its dynamic properties to suit the current mode of locomotion. The greatest challenge in applying EMG to the control of leg prostheses is EMG distortion that is generated due to the fact that leg prostheses must bear the weight of the user. This introduces considerable force/pressure against the socket and motion/compression of the muscle, and may cause an incorrect interpretation of the locomotion mode. This is a significant barrier to the clinical application of EMG pattern classification for neural control of artificial legs, as incorrect classification may cause the user to fall and suffer serious injuries. Therefore, the focus of this research is to investigate and improve the robustness of EMG signal. This includes investigating the nature of distortion and studying its effect on the classification accuracy. Moreover, an EMG distortion detector and filter is proposed that detects distortion by the fact that normally occurring EMG has a normal amplitude distribution and that distortion manifests itself as extremes or outliers of this distribution. The proposed EMG distortion detector and filter was able to remove high amplitudes distortion from EMG signal and it has resulted in 10% locomotion mode classification improvement. Additionally, a new phase detection technique is proposed that improves the reliability of phase detection system being used in phase dependent classifiers.