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Convolutional neural network proposal for wrist position classification from electromyography signals Capítulo uri icon


  • Commonly, electromyography (EMG) signals have been employed for movements or pattern classification. For this, different digital signals processing methods are applied to extract features, before a classification stage. The present work deals with a proposal based on the use of image classification employing deep learning techniques. The images were obtained from a spectrogram analysis as a previous process of the convolutional neural network employment. Then, a classification of five positions from wrist movements is carried out the model. Results showed that the accuracy is comparable to similar techniques employed with a shallow neural network and a deep neural network applied to the same dataset.

fecha de publicación

  • 2020-8-7