Abstract / truncated to 115 words (read the full abstract)

In recent years, a huge body of data generated by various applications in domains like social networks and healthcare have paved the way for the development of high performance models. Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Combined with advancements in electromyography it has given rise to new hand gesture recognition applications, such as human computer interfaces, sign language recognition, robotics control and rehabilitation games. The purpose of this thesis is to develop novel methods for electromyography signal analysis based on deep learning for the problem of hand gesture recognition. Specifically, we focus on methods for data preparation and ... toggle 9 keywords

electromyography hand gesture recognition deep learning convolutional neural networks data augmentation transfer learning EMG semg CNN


Tsinganos, Panagiotis
University of Patras, Greece - Vrije Universiteit Brussel, Belgium
Publication Year
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Nov. 30, 2021

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