Dennis Brueckner1, Beat Göpfert2, Rainer Kiss3, Thomas Muehlbauer4. 1. Division of Movement and Training Sciences/Biomechanics of Sport, University of Duisburg-Essen, Essen, Germany. 2. Center of Biomechanics and Biocalorimetry, Department of Biomedical Engineering, University of Basel, Basel, Switzerland. 3. Department of Health and Social Affairs, FHM Bielefeld - University of Applied Sciences, Bielefeld, Germany. 4. Division of Movement and Training Sciences/Biomechanics of Sport, University of Duisburg-Essen, Essen, Germany. Electronic address: thomas.muehlbauer@uni-due.de.
Abstract
BACKGROUND: Previous research showed changes in amplitude- or time-derived measures of electromyographic (EMG) activity with motor learning. However, an analysis of the EMG spectral content (e.g., via wavelet technique) has not been included in these investigations yet. OBJECTIVE: The aim of this study was to use conventional, amplitude-derived EMG parameters along with modern, wavelet-based time-frequency EMG measures to assess the effects of motor practice on learning a dynamic balance task. METHODS: Nineteen young male adults (mean age: 26 ± 6 years) practiced a dynamic balance task for two days. Delayed retention test was performed on the third day. On a behavioral level, the root-mean-square error (RMSE) of the stability platform angle was calculated and used as outcome measure. On a neuromuscular level, EMG data from the tibialis anterior (TA) and the gastrocnemius medialis (GM) muscle were unilaterally recorded and analysed by calculating the integrated EMG (iEMG) and the EMG intensity (via continuous wavelet transforms). RESULTS: Two days of practice resulted in significantly improved balance performance (i.e., lower RMSE) and TA/GM activation (i.e., reduced iEMG and EMG intensity) that was still present during the retention test on day 3. There was also evidence of practice-related changes in the EMG intensity pattern as indicated by an intensity shift from higher to lower frequency components. CONCLUSIONS: We conclude that motor practice leads to improvements in movement effectiveness as indicated by reduced RMSE and in movement efficiency (i.e., decreased iEMG and EMG intensity, intensity shift). In addition to conventional amplitude-derived EMG parameters, modern, wavelet-based time-frequency EMG measures are appropriate to detect practice-related changes in muscle activation.
BACKGROUND: Previous research showed changes in amplitude- or time-derived measures of electromyographic (EMG) activity with motor learning. However, an analysis of the EMG spectral content (e.g., via wavelet technique) has not been included in these investigations yet. OBJECTIVE: The aim of this study was to use conventional, amplitude-derived EMG parameters along with modern, wavelet-based time-frequency EMG measures to assess the effects of motor practice on learning a dynamic balance task. METHODS: Nineteen young male adults (mean age: 26 ± 6 years) practiced a dynamic balance task for two days. Delayed retention test was performed on the third day. On a behavioral level, the root-mean-square error (RMSE) of the stability platform angle was calculated and used as outcome measure. On a neuromuscular level, EMG data from the tibialis anterior (TA) and the gastrocnemius medialis (GM) muscle were unilaterally recorded and analysed by calculating the integrated EMG (iEMG) and the EMG intensity (via continuous wavelet transforms). RESULTS: Two days of practice resulted in significantly improved balance performance (i.e., lower RMSE) and TA/GM activation (i.e., reduced iEMG and EMG intensity) that was still present during the retention test on day 3. There was also evidence of practice-related changes in the EMG intensity pattern as indicated by an intensity shift from higher to lower frequency components. CONCLUSIONS: We conclude that motor practice leads to improvements in movement effectiveness as indicated by reduced RMSE and in movement efficiency (i.e., decreased iEMG and EMG intensity, intensity shift). In addition to conventional amplitude-derived EMG parameters, modern, wavelet-based time-frequency EMG measures are appropriate to detect practice-related changes in muscle activation.