Abstract
PurposeTo wavelet transform the electromyograms of the vastii muscles and generate wavelet intensity patterns (WIP) of runners. Test the hypotheses: 1) The WIP of the vastus medialis (VM) and vastus lateralis (VL) of one step are more similar than the WIPs of these two muscles, offset by one step. 2) The WIPs within one muscle differ by having maximal intensities in specific frequency bands and these intensities are not always occurring at the same time after heel strike. 3) The WIPs that were recorded form one muscle for all steps while running can be grouped into clusters with similar WIPs. It is expected that clusters might have distinctly different, cluster specific mean WIPs.MethodsThe EMG of the vastii muscles from at least 1000 steps from twelve runners were recorded using a bipolar current amplifier and yielded WIPs. Based on the weights obtained after a principal component analysis the dissimilarities (1-correlation) between the WIPs were computed. The dissimilarities were submitted to a hierarchical cluster analysis to search for groups of steps with similar WIPs. The clusters formed by random surrogate WIPs were used to determine whether the groups were likely to be created in a non-random manner.ResultsThe steps were grouped in clusters showing similar WIPs. The grouping was based on the frequency bands and their timing showing that they represented defining parts of the WIPs. The correlations between the WIPs of the vastii muscles that were recorded during the same step were higher than the correlations of WPIs that were recorded during consecutive steps, indicating the non-randomness of the WIPs.ConclusionsThe spectral power of EMGs while running varies during the stance phase in time and frequency, therefore a time averaged power spectrum cannot reflect the timing of events that occur while running. It seems likely that there might be a set of predefined patterns that are used upon demand to stabilize the movement.
Highlights
Relating electric muscle activity to muscle fiber contractile force generation Walking and running require dynamic motor control to produce the movement and maintain stability
The steps were grouped in clusters showing similar wavelet intensity pattern (WIP)
The grouping was based on the frequency bands and their timing showing that they represented defining parts of the WIPs
Summary
Relating electric muscle activity to muscle fiber contractile force generation Walking and running require dynamic motor control to produce the movement and maintain stability. It is common practice to attribute the fall in mean power frequency of the EMG during fatigue to a proportionate fall in conduction velocity of the motor unit (MU) action potentials [4]. It is still possible that other factors such as de- and recruitment of fibres and change in motor unit firing rates contribute to the fall in mean power frequency during fatigue. Whether different fiber types would be detectable because of their spectral differences was debated in point counterpoint articles [10] [11] This short introduction shows that it is essential to improve our understanding of the spectral properties of the surface EMG and their physiological causes
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