Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions with Temporal Multi-Channel Vision Transformers

Godoy RV, Dwivedi A, Liarokapis M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Transactions on Neural Systems and Rehabilitation Engineering Institute of Electrical and Electronics Engineers (IEEE)

Pages Range: 1-1

DOI: 10.1109/TNSRE.2022.3196622

Abstract

Electromyography (EMG) signals have been used in designing muscle-machine interfaces (MuMIs) for various applications, ranging from entertainment (EMG controlled games) to human assistance and human augmentation (EMG controlled prostheses and exoskeletons). For this, classical machine learning methods such as Random Forest (RF) models have been used to decode EMG signals. However, these methods depend on several stages of signal pre-processing and extraction of hand-crafted features so as to obtain the desired output. In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, in-hand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers. The results obtained are compared, in terms of accuracy and speed of decoding the motion, with RF-based models and Convolutional Neural Networks as a benchmark. The models are trained for 11 subjects in a motion-object specific and motion-object generic way, using the 10-fold cross-validation procedure. This study shows that the performance of MuMIs can be improved by employing DL-based models with raw myoelectric activations instead of developing DL or classic machine learning models with hand-crafted features.

Authors with CRIS profile

Anany Dwivedi Lehrstuhl für Autonome Systeme und Mechatronik

Involved external institutions

University of Auckland

New Zealand (NZ)

How to cite

APA:

Godoy, R.V., Dwivedi, A., & Liarokapis, M. (2022). Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions with Temporal Multi-Channel Vision Transformers. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1-1. https://doi.org/10.1109/TNSRE.2022.3196622

MLA:

Godoy, Ricardo V., Anany Dwivedi, and Minas Liarokapis. "Electromyography Based Decoding of Dexterous, In-Hand Manipulation Motions with Temporal Multi-Channel Vision Transformers." IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022): 1-1.

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