Wearable super-resolution muscle–machine interfacing

Wang, H., Zuo, S., Cerezo-Sánchez, M., Ghahremani Arekhloo, N., Nazarpour, K. and Heidari, H. (2022) Wearable super-resolution muscle–machine interfacing. Frontiers in Neuroscience, 16, 1020546. (doi: 10.3389/fnins.2022.1020546) (PMID:36466163) (PMCID:PMC9714306)

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Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.

Item Type:Articles
Additional Information:This work was partially supported by EPSRC projects EP/X525716/1, EP/X024989/1, EP/X034690/1, and EP/R004242/2. The works of MC-S and NA were supported by the University of Glasgow Scholarship.
Glasgow Author(s) Enlighten ID:Zuo, Dr Siming and Cerezo Sanchez, Ms Maria and Ghahremani arekhloo, Negin and Heidari, Professor Hadi and Wang, Mr Huxi
Authors: Wang, H., Zuo, S., Cerezo-Sánchez, M., Ghahremani Arekhloo, N., Nazarpour, K., and Heidari, H.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Frontiers in Neuroscience
Publisher:Frontiers Media
ISSN (Online):2198-3844
Copyright Holders:Copyright © 2022 Wang, Zuo, Cerezo-Sánchez, Arekhloo, Nazarpour and Heidari
First Published:First published in Frontiers in Neuroscience 16: 1020546
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
317683UKRI EPSRC Impact Acceleration Accounts (IAA) 2022 - 2025Christopher PearceEngineering and Physical Sciences Research Council (EPSRC)EP/X525716/1ENG - Systems Power & Energy