Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG

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. In summary, the smart compression garment based on FMG is a valid alternative to EMG-garments and provides more accurate results at high-speed activity (avoiding the electro-mechanical delay), as well as clearly measures the progress of muscle fatigue over time.

Pressure Sensor System for Customized Scoliosis Braces

“…This approach spans the research and development process from sensor design, to measuring the pressure inside a scoliosis brace ( Figure 1 a,b). The sensor was developed from experience in similar sensors of previous projects [ 11 , 12 , 13 , 14 , 25 , 26 , 27 ]. Due to the foam padding inside the scoliosis brace and the knowledge [ 25 ] of how materials placed next to a piezoresistive sensor affect their electrical properties, the two foams involved had to be tested and characterized mechanically.…”

“…Piezoresistive sensors are relatively cheap but nonetheless sufficiently accurate for measurement of pressure between skin and garments or shoes. For example, Belbasis and Fuss [ 11 , 12 ] and Belbasis et al [ 13 , 14 ] used customized piezoresistive polymers to measure the pressure between the skin and a compression garment for assessment of muscle activity and fatigue (mechanomyography, force-myography (FMG)). Other FMG applications with piezoresistive sensors, encompassing off-the-shelf force sensing resistive (FSR) sensors such as single, multiple, and matrix sensors, have been reported for different applications [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Effects of smart garments on the well-being of athletes: a scoping review protocol

“…► What functions do smart garments offer for professional athletes? [34][35][36] ► Microclimate temperature and humidity monitoring 37 38 Other smart functions ► Smart cooling 34 38 39 ► Smart compression 40 on December 1, 2020 by guest. Protected by copyright.…”

An Ultra-Sensitive Modular Hybrid EMG–FMG Sensor with Floating Electrodes

“…In [ 18 ], the author demonstrated the feasibility of a piezoresistive array armband for hand gesture recognition with three FSR-based sensors. Since both the EMG and FMG can be used for prosthesis control, there are also many studies on the comparison of the EMG and FMG; in some research, it has been shown that the FMG is more stable than EMG over time [ 19 ], while in other research, the EMG is more accurate for muscle fatigue modeling [ 20 ].…”