Improving the performance of a neural-machine interface for artificial legs using prior knowledge of walking environment

A previously developed neural-machine interface (NMI) based on neuromuscular-mechanical fusion has showed promise for recognizing user locomotion modes; however, errors of NMI during mode transitions were observed, which may challenge its real application. This study aimed to investigate whether or not the prior knowledge of walking environment could further improve the NMI performance. Linear Discriminant Analysis (LDA)-based classifiers were designed to identify user intent based on electromyographic (EMG) signals from residual muscles of leg amputees and ground reaction force (GRF) measured from the prosthetic leg. The prior knowledge of the terrain in front of the user adjusted the prior possibility in the discriminant function. Therefore, the boundaries of LDA were adaptive to the prior knowledge of the walking environment. This algorithm was evaluated on a dataset collected from one patient with a transfemoral (TF) amputation. The preliminary results showed that the NMI with adaptive prior possibilities outperformed the NMI without using the prior knowledge; it produced 98.7% accuracy for identifying tested locomotion modes, accurately predicted all the task transitions with 261–390 ms prediction time, and generated stable decision during task transitions. These results indicate the potential of using prior knowledge about walking environment to further improve the NMI for prosthetic legs.