Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers

Godoy RV, Lahr GJ, Dwivedi A, Reis TJ, Polegato PH, Becker M, Caurin GA, Liarokapis M (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: IEEE Computer Society

Book Volume: 2022-August

Conference Proceedings Title: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

Event location: Seoul KR

ISBN: 9781665458498

DOI: 10.1109/BioRob52689.2022.9925307

Abstract

With an increasing use of robotic and bionic devices for the execution of everyday life, complex tasks, Electromyography (EMG) based interfaces are being explored as candidate technologies for facilitating an intuitive interaction with such devices. However, EM G-based interfaces typically require appropriate features to be extracted from the raw EMG signals using a plethora of feature extraction methods to achieve excellent performance in practical applications. To select an appropriate feature set that will lead to significant EMG-based decoding performance, a deep understanding of available methods and the human musculoskeletal system is needed. To overcome this issue, researchers have proposed the use of deep learning methods for automatically extracting complex features directly from the raw EMG data. In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands. The performance of this method is evaluated and compared with other well-known methods, employing the open-Access Ninapro dataset.

Authors with CRIS profile

Anany Dwivedi Lehrstuhl für Autonome Systeme und Mechatronik

Involved external institutions

University of Auckland NZ New Zealand (NZ) University of São Paulo / Universidade de São Paulo (USP) BR Brazil (BR) Italian Institute of Technology / Istituto Italiano di Tecnologia (IIT) IT Italy (IT)

How to cite

APA:

Godoy, R.V., Lahr, G.J., Dwivedi, A., Reis, T.J., Polegato, P.H., Becker, M.,... Liarokapis, M. (2022). Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. Seoul, KR: IEEE Computer Society.

MLA:

Godoy, Ricardo V., et al. "Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers." Proceedings of the 9th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2022, Seoul IEEE Computer Society, 2022.

BibTeX: Download