EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
Akira Furui,
Takuya Igaue,
Toshio Tsuji
Abstract:Electromyogram (EMG) has been utilized to interface signals for prosthetic hands and information devices owing to its ability to reflect human motion intentions. Although various EMG classification methods have been introduced into EMG-based control systems, they do not fully consider the stochastic characteristics of EMG signals. This paper proposes an EMG pattern classification method incorporating a scale mixture-based generative model. A scale mixture model is a stochastic EMG model in which the EMG varian… Show more
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