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Publication
Improved gesture recognition based on sEMG signals and TCN
Book Contribution - Book Chapter Conference Contribution
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach
electromyography-based hand gesture recognition as a sequence classification
problem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
electromyography-based hand gesture recognition as a sequence classification
problem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
Book: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Series: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages: 1169-1173
Number of pages: 5
Publication year:2019
Keywords:sEMG, Gesture Recognition, Deep Learning, CNN, TCN
Accessibility:Closed