A myoelectric digital twin for fast and realistic modelling in deep learning

Kostiantyn Maksymenko (), Alexander Kenneth Clarke, Irene Mendez Guerra, Samuel Deslauriers-Gauthier and Dario Farina ()
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Kostiantyn Maksymenko: Neurodec
Alexander Kenneth Clarke: Imperial College London
Irene Mendez Guerra: Imperial College London
Samuel Deslauriers-Gauthier: Neurodec
Dario Farina: Imperial College London

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.

Date: 2023
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DOI: 10.1038/s41467-023-37238-w

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