Development of a Myoelectric Controller based on Knee Angle Estimation

This paper presents the development of a bioinstrumentation system for the acquisition and pre-processing of surface electromyographic (SEMG) signals, as well as the proposal of a myoelectric controller for leg prostheses, using algorithms for feature extraction and classification of myoelectric patterns. The implemented microcontrolled bioinstrumentation system is capable of recording up to four SEMG channels, and one electrogoniometer channel. The proposed neural myoelectric controller is capable of predicting the intended knee joint angle from the measured SEMG singals. The controller is designed in three stages: feature extraction, using auto-regressive model and amplitude histogram; feature projection, using self organizing maps; and pattern classification, using a Levenberg-Marquadt neural network. The use of SEMG signals and additional mechanical information such as that provided by the electrogoniometer may improve precision in the control of leg prostheses. Preliminary results are presented.