Knowledge base generation and its implementation for control of above knee prosthetic device based on SEMG and knee flexion angle

Abstract

Advanced intelligent knee prosthesis for trans-femoral amputees requires a versatile control strategy and associated control algorithm. Control strategy was evolved by mapping surface EMG (SEMG) from four muscles of healthy lower limb of a unilateral trans-femoral amputee and knee flexion angles (KFA) during various phases of a gait cycle. The SEMG and KFA are calibrated to three walking speeds modes i.e., slow, normal and fast. Sensor mechanisms feeds real-time data to controller to generate an appropriate control output signal based on available knowledgebase which calculates the patient's gait parameters i.e., KFA and SEMG from associated muscles during the corresponding phase of walk. Important aspect of control strategy is the development of knowledgebase proves that the SEMG signal generates recognisable pattern for change in walking speed when signals were analysed in time and frequency domain. These patterns were quantified and utilised for controlling electro-pneumatic knee joint.