Over the past years, Deep Learning methods have shown promising results 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 architectures 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.
Tsinganos, P, Skodras, A, Cornelis, B & Jansen, B 2018, Deep Learning in Gesture Recognition Based on sEMG Signals. in F Ring, W-C Siu, L-P Chau, L Wang & T Tang (eds), Learning Approaches in Signal Processing. Learning Approaches in Signal Processing, Pan Stanford Publishing. https://doi.org/10.1201/9780429061141-23
Tsinganos, P., Skodras, A., Cornelis, B., & Jansen, B. (2018). Deep Learning in Gesture Recognition Based on sEMG Signals. In F. Ring, W.-C. Siu, L.-P. Chau, L. Wang, & T. Tang (Eds.), Learning Approaches in Signal Processing (Learning Approaches in Signal Processing). Pan Stanford Publishing. https://doi.org/10.1201/9780429061141-23
@inbook{c1c140398aaa4fbda2006e8014c4f226,
title = "Deep Learning in Gesture Recognition Based on sEMG Signals",
abstract = "Over the past years, Deep Learning methods have shown promising results 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 architectures 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.",
author = "Panagiotis Tsinganos and Athanassios Skodras and Bruno Cornelis and Bart Jansen",
year = "2018",
doi = "10.1201/9780429061141-23",
language = "English",
isbn = "9780429590320",
series = "Learning Approaches in Signal Processing",
publisher = "Pan Stanford Publishing",
editor = "Francis Ring and Wan-Chi Siu and Lap-Pui Chau and Liang Wang and Tieniu Tang",
booktitle = "Learning Approaches in Signal Processing",
}