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Wearable MMG-plus-one armband: evaluation of normal force on mechanomyography (MMG) to enhance human-machine interfacing

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Title: Wearable MMG-plus-one armband: evaluation of normal force on mechanomyography (MMG) to enhance human-machine interfacing
Authors: Mancero Castillo, C
Wilson, S
Vaidyanathan, R
Atashzar, F
Item Type: Journal Article
Abstract: In this paper, we introduce a new mode of mechanomyography (MMG) signal capture for enhancing the performance of human-machine interfaces (HMIs) through modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the sensor-skin contact area. We detail the mechatronic design, experimental validation, and user study of an armband with embedded acoustic sensors demonstrating this capacity. The design is motivated by the nonlinear viscoelasticity of the tissue, which increases with the normal surface pressure. This, in theory, results in higher conductivity of mechanical waves and hypothetically allows to interface with deeper muscle; thus, enhancing the discriminative information context of the signal space. Ten subjects (seven able-bodied and three trans-radial amputees) participated in a study consisting of the classification of hand gestures through MMG while increasing levels of contact force were administered. Four MMG channels were positioned around the forearm and placed over the flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris muscles. A total of 852 spectrotemporal features were extracted (213 features per each channel) and passed through a Neighborhood Component Analysis (NCA) technique to select the most informative neurophysiological subspace of the features for classification. A linear support vector machine (SVM) then classified the intended motion of the user. The results indicate that increasing the normal force level between the MMG sensor and the skin can improve the discriminative power of the classifier, and the corresponding pattern can be user-specific. These results have significant implications enabling embedding MMG sensors in sockets for prosthetic limb control and HMI.
Issue Date: 1-Jan-2021
Date of Acceptance: 8-Dec-2020
URI: http://hdl.handle.net/10044/1/84931
DOI: 10.1109/TNSRE.2020.3043368
ISSN: 1534-4320
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 196
End Page: 205
Journal / Book Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume: 29
Copyright Statement: © 2020 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Keywords: 0903 Biomedical Engineering
0906 Electrical and Electronic Engineering
Biomedical Engineering
Publication Status: Published
Open Access location: https://ieeexplore.ieee.org/document/9286496
Online Publication Date: 2020-12-08
Appears in Collections:Mechanical Engineering
Faculty of Engineering

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