Development of Next-generation Compression Apparel

Belbasis, Aaron; Fuss, Franz Konstantin

doi:10.1016/j.protcy.2015.07.015

Cited by 11 publications

(14 citation statements)

References 11 publications

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“…they must be positioned accurately, preferably “in the midline of the muscle belly between the nearest innervation zone and the myotendinous junction furthest from this zone” (De Luca, 1997 ), since even small movements away from the innervation zone (e.g., 10% of the muscle length) reduce signal amplitude considerably (Belbasis and Fuss, 2015 );…”

Section: Wearable Technologies Designed For Individual Consumersmentioning

confidence: 99%

“…Therefore, EMG fabrics designed to assess muscular activity are considered inaccurate. An alternative and promising approach involves incorporation of pressure sensors into compression garments (Belbasis and Fuss, 2015 ).…”

Section: Wearable Technologies Designed For Individual Consumersmentioning

confidence: 99%

“…Neuromuscular fatigue (at least for the legs) as reflected in a countermovement jump (Cormack et al, 2008 ) while wearing a low-cost and pressure-sensitive insole (Tan et al, 2015 ) or a compression garment equipped with pressure sensors could be assessed (Belbasis and Fuss, 2015 ).…”

Section: Recommendations Concerning Wearables For Athletesmentioning

confidence: 99%

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Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

et al. 2016

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Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.

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Section: Wearable Technologies Designed For Individual Consumersmentioning

confidence: 99%

Section: Wearable Technologies Designed For Individual Consumersmentioning

confidence: 99%

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Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

et al. 2016

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“…The most common sensors used for FMG purposes are off-the-shelf FSR (force sensing resistive) sensors, either as single sensors, several sensors ( Connan et al, 2016 ) or sensor matrix arrays ( Zhou et al, 2017 ), that are preloaded, compressed either by tight fitting garments or by elastic bands to the surface of the relevant muscles ( Lukowicz et al, 2006 ; McLaren et al, 2010 ; Zhou et al, 2017 ), Velcro bracelets ( Connan et al, 2016 ), integrated in a textile sleeve ( Ogris et al, 2007 ), equipped with mechanical preload adjustments ( Li et al, 2012 ), or placed inside a forearm orthosis ( Wininger et al, 2008 ). Belbasis and Fuss (2015) and Belbasis et al (2015a , b) used several customized piezoresistive polymer sensors sandwiched between compression garment and skin. Meyer et al (2006) applied a capacitance pressure sensor array embedded in textiles.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this paper was to explore an existing prototype of pressure sensor-based garment ( Belbasis and Fuss, 2015 ; Belbasis et al, 2015a , b ) for opportunities in performance analysis, specifically muscle activation and fatigue, and to validate the prototype against EMG, used as the gold standard for muscle performance assessment.…”

Section: Introductionmentioning

confidence: 99%

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

Belbasis

Fuss

2018

Front. Physiol.

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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.

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Ex Vivo Biosignatures

Moghaddam

Lowe

2018

Health and Wellness Measurement Approaches for Mobile Healthcare

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Development of Next-generation Compression Apparel

Cited by 11 publications

References 11 publications

Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

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

Ex Vivo Biosignatures

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