Across-user adaptation for a powered lower limb prosthesis

Pattern recognition algorithms have been used to control powered lower limb prostheses because they are capable of identifying the intent of the amputee user and therefore can provide a method for seamlessly transitioning between the different locomotion modes of the prosthesis. However, one major limitation of current algorithms is that they require subject-specific data from the individual user. These data are difficult and time-consuming to collect and consequently these algorithms do not generalize well across users. We have developed an adaptive pattern recognition algorithm that automatically learns new subject-specific data acquired from a novel user during ambulation. We tested this adaptive algorithm with one transfemoral amputee subject walking on a powered knee-ankle prosthesis. Before adaptation, the algorithm, which was initially trained with data from two other transfemoral amputee subjects, made critical errors that prevented continuous ambulation. With adaptation, error rates dropped from 4.21% before adaptation to 1.25% after adaptation, and allowed the novel amputee user to complete all mode transitions. This study demonstrates that adaptation can decrease error rates over time and can allow pattern recognition algorithms to generalize to novel users.