Ghahremani Arekhloo, N., Parvizi, H., Zuo, S., Wang, H. , Nazarpour, K. and Heidari, H. (2024) Investigating the Advantages of Magnetomyography in Assistive Healthcare Technology. In: 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Istanbul, Turkey, 04-07 Dec 2023, ISBN 9798350326499 (doi: 10.1109/icecs58634.2023.10382891)
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Abstract
Assistive healthcare technologies and prosthetics are crucial for individuals with muscle impairments. In 2005, the number of limb losses from trauma exceeded 700,000, projected to double by 2050, affecting approximately 1,326,000 civilians. Understanding the fundamental principles of muscle function, therefore, is key to developing innovative assistive technologies that can improve the quality of life for people with disabilities. Surface electromyography (sEMG), measuring electrical muscle activity, has long been a common tool in assistive technologies, but various obstacles have limited its widespread application. Capturing sEMG signals via the skin and subcutaneous fat poses a main challenge as they act as a low-pass filter and lead to the loss of critical information. Thus, new alternative technologies are needed to address this challenge. Magnetomyography (MMG) is a technology that can noninvasively measure magnetic muscle signals. Unlike sEMG, MMG signals are not affected by various tissues as they are transparent for magnetic signals. This paper presents the fundamental scenarios, including fat thickness on the EMG and MMG signals, with finite element (FE) simulations using COMSOL. The effects of 50-750 μ m fat on the recorded electrical and magnetic signals have been evaluated. The results indicate that by increasing fat thickness to 250μ m, the electrical signals decrease 66%, while MMG signals decline by 12%. Hence, the MMG can provide more accurate measurements of muscle activity for control strategies in prosthetic limbs.
| Item Type: | Conference Proceedings |
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| Additional Information: | The research leading to these results received funding from the Scottish Research Partnership in Engineering - SRPe (PEER1718/03). This work was partially supported by EPSRC IAA projects EP/X5257161/1. The authors are also thankful to the University of Glasgow for funding received under the Glasgow Exchange Knowledge (GKE) Fund 2017/2018. |
| Keywords: | Assistive healthcare technology, electrical signal, electromyography, EMG, fat effect, MMG, magnetic signal, magnetomyography, prosthetics. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Wang, Huxi and Parvizi, Mr Hossein and Ghahremani arekhloo, Negin and Heidari, Professor Hadi and Zuo, Siming |
| Authors: | Ghahremani Arekhloo, N., Parvizi, H., Zuo, S., Wang, H., Nazarpour, K., and Heidari, H. |
| College/School: | College of Science and Engineering College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering |
| Journal Name: | 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS) |
| Publisher: | IEEE |
| ISBN: | 9798350326499 |
| Copyright Holders: | Copyright © 2023 IEEE |
| First Published: | First published in 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS) |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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