Spectral Collaborative Representation Based Classification by Circulants and its Application to Hand Gesture and Posture Recognition from Electromyography Signals
نویسندگان
چکیده
In this study we introduce and demystify a novel signal pattern recognition method, Spectral Collaborative Representation based Classification (SCRC) and demonstrate its application for recognition of hand gestures and postures using Electromyography sensors. A recently released Thalmic Labs MYO armband is used to gather muscle electromyography signals. Along with the new signal pattern classification algorithm, we also introduce a training approach which implicitly embeds the gesture boundaries in a training dictionary that allows continous gesture and posture recognition. The worst recognition accuracy we obtained for a set of experiments is over 97% which is the highest recognition results in the literature where bio-signals
منابع مشابه
Spectral Collaborative Representation based Classification for Hand Gestures recognition on Electromyography Signals
In this study, we introduce a novel variant and application of the Collaborative Representation based Classification in spectral domain for recognition of the hand gestures using the raw surface Electromyography signals. The intuitive use of spectral features are explained via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize...
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