A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autorinnen und Autoren: von Tscharner V, Ullrich M, Mohr M, Comaduran Marquez D, Nigg BM
Zeitschrift: PLoS ONE
Jahr der Veröffentlichung: 2018
ISSN: 1932-6203


Abstract

Purpose

Purpose


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To 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.




Methods

Methods


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The
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.




Results

Results


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The
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.




Conclusions

Conclusions


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The
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.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Ullrich, Martin
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


Zitierweisen

APA:
von Tscharner, V., Ullrich, M., Mohr, M., Comaduran Marquez, D., & Nigg, B.M. (2018). A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns. PLoS ONE. https://dx.doi.org/10.1371/journal.pone.0195125

MLA:
von Tscharner, Vinzenz, et al. "A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns." PLoS ONE (2018).

BibTeX: 

Zuletzt aktualisiert 2019-03-01 um 15:10