A strategy for labeling data for the neural adaptation of a powered lower limb prosthesis

Pattern recognition algorithms that use EMG signals have been proposed to help control powered lower limb prostheses. These algorithms do not automatically compensate for disturbances in EMG signals, resulting in deterioration of algorithm accuracies. Supervised adaptive pattern recognition algorithms can solve this problem, but require correct labeling of new data. Information from embedded mechanical sensors can be compared to the characteristic gait profiles of the different modes to identify the mode of the user's most recent stride and provide a label for new data. The purpose of this study was to develop a gait pattern estimator (GPE) that could automatically make such a comparison. The GPE output was used to supervise an adaptive EMG-based pattern recognition algorithm. Our results indicate that using GPE-based adaptation helped prevent classification errors that would otherwise occur between experimental sessions. The GPE could accurately label new data with a low error rate of approx. 2%. The low error rate of the GPE was reflected in the accuracy of an adapted pattern recognition algorithm. The error rate of the adapted algorithm that was supervised by the GPE was not significantly different from one that used perfect supervision.