Effect of additional mechanical sensor data on an EMG-based pattern recognition system for a powered leg prosthesis

Powered lower limb prostheses can improve amputees' ability to traverse stairs and ramps by providing positive mechanical work at the knee and ankle joint. EMG signals have been proposed as one way of providing seamless mode transitions by using them in combination with embedded mechanical sensors as inputs to a pattern recognition system that predicts the user's desired locomotion mode. In this study, we have expanded the amount of mechanical sensor information to include data from an additional five degrees of freedom in the load cell, as well as calculated thigh and shank angles. The purpose of this study was to determine the impact of this additional information on the performance of an EMG-based pattern recognition system designed to predict the desired locomotion mode. Our results indicate that including the additional mechanical sensor signals decreased the error rates of the system for both steady-state and transitional steps when compared to the reduced sensor set. We also found that EMG still decreased the error rate of the system, but to a lesser extent when using the additional mechanical sensors.