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

Engineering and Technology

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

K. Maksymenko, A. K. Clarke, et al.

Discover a groundbreaking approach to muscle signal decoding with the Myoelectric Digital Twin, developed by Kostiantyn Maksymenko and colleagues. This innovative model simulates EMG signals, enabling the creation of extensive, high-quality datasets for deep learning in human-machine interfaces.

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Playback language: English
Abstract
Deep learning shows high potential for advanced muscle signal decoding in human-machine interfaces but requires large, high-quality annotated datasets, which are expensive to acquire. This paper introduces a Myoelectric Digital Twin – a realistic and fast computational model for simulating EMG signals. This model enables the creation of arbitrarily large, perfectly annotated datasets, accelerating the development of human-machine interfaces by facilitating deep learning training.
Publisher
Nature Communications
Published On
Mar 23, 2023
Authors
Kostiantyn Maksymenko, Alexander Kenneth Clarke, Irene Mendez Guerra, Samuel Deslauriers-Gauthier, Dario Farina
Tags
deep learning
Myoelectric Digital Twin
EMG signals
human-machine interfaces
data simulation
annotated datasets
muscle signal decoding

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