Muscle performance investigated with a novel smart compression garment based on pressure sensor force myography and its validation against EMG

Belbasis, A and Fuss, F 2018, 'Muscle performance investigated with a novel smart compression garment based on pressure sensor force myography and its validation against EMG', Frontiers in Physiology, vol. 9, pp. 1-13.


Document type: Journal Article
Collection: Journal Articles

Title Muscle performance investigated with a novel smart compression garment based on pressure sensor force myography and its validation against EMG
Author(s) Belbasis, A
Fuss, F
Year 2018
Journal name Frontiers in Physiology
Volume number 9
Start page 1
End page 13
Total pages 13
Publisher Frontiers Research Foundation
Abstract Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD) of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue) comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency) obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD) showed a higher time dependency (R2 = 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle activity
Subject Physiology not elsewhere classified
Psychology not elsewhere classified
Medical Physiology not elsewhere classified
Keyword(s) Crank polar diagram
Cycling
EMG
Force myography
Fractal dimension
Muscle fatigue
Pressure sensors
Smart compression garment
DOI - identifier 10.3389/fphys.2018.00408
Copyright notice Copyright © 2018 Belbasis and Fuss. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License (CC BY).
ISSN 1664-042X
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