Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG
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), compri...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-04-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fphys.2018.00408/full |
| _version_ | 1818941034151804928 |
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| author | Aaron Belbasis Franz Konstantin Fuss |
| author_facet | Aaron Belbasis Franz Konstantin Fuss |
| author_sort | Aaron Belbasis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T06:49:07Z |
| format | Article |
| id | doaj.art-2123ed8444e7441a82dc2472bb574237 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-20T06:49:07Z |
| publishDate | 2018-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-2123ed8444e7441a82dc2472bb5742372022-12-21T19:49:37ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-04-01910.3389/fphys.2018.00408330696Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMGAaron Belbasis0Franz Konstantin Fuss1School of Engineering, RMIT University, Melbourne, VIC, AustraliaSmart Equipment Engineering and Wearable Technology Program, Centre for Design Innovation, Swinburne University of Technology, Melbourne, VIC, AustraliaMuscle 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.http://journal.frontiersin.org/article/10.3389/fphys.2018.00408/fullsmart compression garmentforce myographypressure sensorsEMGcyclingcrank polar diagram |
| spellingShingle | Aaron Belbasis Franz Konstantin Fuss Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG Frontiers in Physiology smart compression garment force myography pressure sensors EMG cycling crank polar diagram |
| title | Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG |
| title_full | Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG |
| title_fullStr | Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG |
| title_full_unstemmed | Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG |
| title_short | Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG |
| title_sort | muscle performance investigated with a novel smart compression garment based on pressure sensor force myography and its validation against emg |
| topic | smart compression garment force myography pressure sensors EMG cycling crank polar diagram |
| url | http://journal.frontiersin.org/article/10.3389/fphys.2018.00408/full |
| work_keys_str_mv | AT aaronbelbasis muscleperformanceinvestigatedwithanovelsmartcompressiongarmentbasedonpressuresensorforcemyographyanditsvalidationagainstemg AT franzkonstantinfuss muscleperformanceinvestigatedwithanovelsmartcompressiongarmentbasedonpressuresensorforcemyographyanditsvalidationagainstemg |