Journal of bodywork and movement therapies · 2020
Effects of vibration therapy on neuromuscular efficiency & features of the EMG signal based on endurance test.
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Alam Mohd Mukhtar, Khan Abid Ali, Farooq Mohd
Аннотация
BACKGROUND: Vibration Therapy (VT) stimulate the muscle spindles, which in turn enhances its afferent activities.
OBJECTIVE: The present study was designed to investigate the effect of VT at 23 and 35 Hz on muscle performance. The EMG features (six time-domain (TD) and four frequency-domain (FD)) and a new formula for computing neuromuscular performance were used as dependent variables to evaluate the effect of VT.
METHOD: The EMG recording was performed at 50% MVC during grip endurance test before and after VT. The EMG features were extracted out of raw EMG signals acquired from four forearm muscles, viz., flexor digitorum superficialis (FDS); flexor carpi ulnaris (FCU); extensor carpi radialis brevis (ECRB); and extensor carpi ulnaris (ECU) in supine position. Fatigue assessments were evaluated based on the pattern of TD and FD features.
RESULTS: Statistical analysis showed a significant difference in the effect of vibration exposure frequency on IEMG (p < 0.001), MAV (p = 0.041), SSI (p = 0.032), and WL (p < 0.001) of FCU muscle. In addition, the greatest increase in neuromuscular efficiency (NME) was observed in the performance of ECRB after 35 Hz of VT and ECU muscles after 23 Hz of VT.
CONCLUSIONS: The features of EMG signals could be used for fatigue analysis. However, the slope based on the median frequency regression line may be the best feature for fatigue assessment.
OBJECTIVE: The present study was designed to investigate the effect of VT at 23 and 35 Hz on muscle performance. The EMG features (six time-domain (TD) and four frequency-domain (FD)) and a new formula for computing neuromuscular performance were used as dependent variables to evaluate the effect of VT.
METHOD: The EMG recording was performed at 50% MVC during grip endurance test before and after VT. The EMG features were extracted out of raw EMG signals acquired from four forearm muscles, viz., flexor digitorum superficialis (FDS); flexor carpi ulnaris (FCU); extensor carpi radialis brevis (ECRB); and extensor carpi ulnaris (ECU) in supine position. Fatigue assessments were evaluated based on the pattern of TD and FD features.
RESULTS: Statistical analysis showed a significant difference in the effect of vibration exposure frequency on IEMG (p < 0.001), MAV (p = 0.041), SSI (p = 0.032), and WL (p < 0.001) of FCU muscle. In addition, the greatest increase in neuromuscular efficiency (NME) was observed in the performance of ECRB after 35 Hz of VT and ECU muscles after 23 Hz of VT.
CONCLUSIONS: The features of EMG signals could be used for fatigue analysis. However, the slope based on the median frequency regression line may be the best feature for fatigue assessment.