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Deep Learning in Gesture Recognition Based on sEMG Signals Host Publication: Learning Approaches in Signal Processing Authors: P. Tsinganos, A. Skodras, B. Cornelis and B. Jansen Publisher: Pan Stanford Publication Date: Sep. 2018
Abstract: Over the past years, Deep Learning methods have shown promising re- sults to a wide range of research fields including image classification and natural language processing. Their increased success rates have drawn the attention of many researchers from various domains. This chapter investigates the application of Deep Learning methods to the problem of electromyography-based gesture recognition. A signal processing pipeline based on Deep Learning is presented through examples taken from the literature, whereas the details of state-of-the-art neural network archi- tectures are discussed. In addition, this chapter illustrates a few ways adopted from image classification tasks that visualize what the neural network learns. Finally, new approaches are proposed and evaluated with publicly available datasets.
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