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.

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Keywords EMG, Gaussian mixture model, Myoelectric signals, Pattern recognition, Prosthesis
Conference A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Citation
Chan, A, & Englehart, K.B. (2003). Continuous Classification of Myoelectric Signals for Powered Prostheses using Gaussian Mixture Models. Presented at the A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.