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Regression of Hand Movements from sEMG Data with Recurrent Neural Networks | IEEE Conference Publication | IEEE Xplore

Regression of Hand Movements from sEMG Data with Recurrent Neural Networks


Abstract:

Most wearable human-machine interfaces concerning hand movements only focus on classifying a limited number of hand gestures. With the introduction of deep learning, surf...Show More

Abstract:

Most wearable human-machine interfaces concerning hand movements only focus on classifying a limited number of hand gestures. With the introduction of deep learning, surface electromyography based hand gesture classification systems improved drastically. Therefore, it is worth investigating whether the classification can be replaced by a movement regression of all the different movable hand parts. As recurrent neural networks based approaches have proven their abilities of solving the classification problem we also choose them for the regression problem. Experiments were conducted with multiple different network architectures on several databases. Furthermore, due to the lack of a reliable measure to compare different gesture regression approaches we propose an interpretable and reproducible new error measure that can even handle noisy ground truth data. The results reveal the general possibility of regressing detailed hand movements. Even with the relatively simple networks the hand gestures can be regressed quite accurately.
Date of Conference: 20-24 July 2020
Date Added to IEEE Xplore: 27 August 2020
ISBN Information:

ISSN Information:

PubMed ID: 33018825
Conference Location: Montreal, QC, Canada

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