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A Hilbert curve based representation of sEMG signals for gesture recognition Host Publication: Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP) Authors: P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras Publisher: IEEE Publication Date: Aug. 2019 ISBN: 978-1-7281-3253-2
Abstract: Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by a CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3%.
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