ArticlePloS one2018
A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 32 citations in OpenAlex.
- Time-Frequency Analysis of Muscle Activation Patterns during the Instep Kick in Elite and Amateur Soccer Players.Journal of sports science & medicine · 2026Article
- Comparison of Time-Frequency Characteristics of Lower Limb EMG Signals Among Different Foot Strike Patterns During Running Using the EEMD Algorithm.Life (Basel, Switzerland) · 2025Article
- The effect of prolonged walking on leg muscle activity patterns and vulnerability to perturbations.Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology · 2023Article
- Changes in ankle work, foot work, and tibialis anterior activation throughout a long run.Journal of sport and health science · 2022Article
- Frequency Shifts in Muscle Activation during Static Strength Elements on the Rings before and after an Eccentric Training Intervention in Male Gymnasts.Journal of functional morphology and kinesiology · 2022Article
- Age-related differences in calf muscle recruitment strategies in the time-frequency domain during walking as a function of task demand.Journal of applied physiology (Bethesda, Md. : 1985) · 2021Article
- Soft-tissue vibration and damping response to footwear changes across a wide range of anthropometrics in running.PloS one · 2021Article
- Overcoming randomness does not rule out the importance of inherent randomness for functionality.Journal of biosciences · 2019Review
- Probing Corticospinal Control During Different Locomotor Tasks Using Detailed Time-Frequency Analysis of Electromyograms.Frontiers in neurology · 2019Article
- Article
- Beta, gamma band, and high-frequency coherence of EMGs of vasti muscles caused by clustering of motor units.Experimental brain research · 2018Article
- Development of a multichannel current-EMG system for coherence modulation with visual biofeedback.PloS one · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.