Myoelectric control can be used for a variety of applications including powered protheses and different human computer interface systems. The aim of this study is to investigate the formulation of myoelectric control as a multi-class distance-based classification of multidimensional sequences. More specifically, we investigate (1) estimation of multi-muscle activation sequences from multi-channel electromyographic signals in an online manner, and (2) classification using a distance metric based on multi-dimensional dynamic time warping. Subject-specific results across 5 subjects executing 10 different hand movements showed an accuracy of 95% using offline extracted trajectories and an accuracy of 84% using online extracted trajectories.
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