Continuous classification of myoelectric signals for powered prostheses using Gaussian mixture models

Pattern recognition is a, key element of myoelectrically controlled prostheses. Improvements in classification accuracy have been achieved using various feature extraction and classification methodologies. In this paper, it is demonstrated that using a simple and direct approach can achieve high classification accuracy, while maintaining a low computational load; important characteristics for a real-time embedded system. An average classification accuracy of 94.06% was achieved for a six class problem, using a single mixture Gaussian mixture model, along with majority vote post-processing.