Electrode-shift tolerant myoelectric movement-pattern classification using extreme learning for adaptive sparse representations

Myoelectric signal patterns can be used to predict the intended movements of amputees for prosthesis activation. Real-world prosthesis use introduces a variety of unpredictable conditional influences on these patterns, hindering the performance of classification algorithms and potentially leading to device abandonment. We have discovered a state-of-the-art classification method which is significantly more tolerant to these conditional influences. In our prior work, we presented a robust sparsity-based adaptive classification method that is tolerant to pattern deviations resulting from untrained limb positions and the prosthesis load. Herein, we demonstrate that this method is tolerant to the shifting or misalignment of the contact-electrode array which occurs during prosthesis use. We demonstrate the robustness of this approach in untrained electrode-site locations for amputee and able-bodied subjects, and report significant performance improvements over conventional myoelectric pattern recognition approaches. By showing that a single, unified method is robust across a variety of real-world condition spaces, clinicians are more likely to incorporate this method into myoelectric prosthesis controllers, resulting in improved utility and increased adoption among amputee users.