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3D Muscle Networks based on Vibrational Mechanomyography

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Title: 3D Muscle Networks based on Vibrational Mechanomyography
Authors: Mancero Castillo, CS
Atashzar, F
Vaidyanathan, R
Item Type: Journal Article
Abstract: Objective: Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography (EMG), which can limit the depth of muscle and spatial information acquisition across muscle fibers.
Approach: We introduce three-dimensional muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands.
We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.
Main Results: Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction.
No statistical decrease in signal strength was observed with higher contact force.
Significance: Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification.
Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue three-dimensional models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.
Issue Date: 17-Nov-2023
Date of Acceptance: 10-Aug-2023
URI: http://hdl.handle.net/10044/1/107073
DOI: 10.1088/1741-2552/ad017c
ISSN: 1741-2552
Publisher: IOP Publishing
Journal / Book Title: Journal of Neural Engineering
Volume: 20
Issue: 6
Copyright Statement: © 2023 IOP Publishing Ltd. Accepted Manuscript will be available for reuse under a CC BY-NC-ND 3.0 licence after the 12 month embargo period. After the embargo period, everyone is permitted to use copy and redistribute this article for non-commercial purposes only, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by-nc-nd/3.0
Publication Status: Published
Article Number: ARTN 066008
Online Publication Date: 2023-10-09
Appears in Collections:Mechanical Engineering
Bioengineering
Faculty of Engineering



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