A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns
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 W...
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| description | 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.
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.
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.
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. |
| doi_str_mv | 10.1371/journal.pone.0195125 |
| format | Article |
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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.
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.
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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0195125</identifier><identifier>PMID: 29668731</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Analysis ; Athletes ; Biology and Life Sciences ; Cluster Analysis ; Correlation ; Correlation analysis ; Diabetes ; Electromyography ; Electrophysiology ; EMG ; Engineering and Technology ; Frequencies ; Frequency analysis ; Human performance ; Humans ; Hypotheses ; Kinesiology ; Laboratories ; Leg muscles ; Male ; Medicine and Health Sciences ; Muscle contraction ; Muscle Contraction - physiology ; Muscle function ; Muscle, Skeletal - physiology ; Muscles ; Physical Sciences ; Physiological aspects ; Physiological research ; Physiology ; Principal components analysis ; Research and Analysis Methods ; Runners (Sports) ; Running ; Signal processing ; Time-frequency analysis ; Walking ; Wavelet analysis ; Wavelet transforms ; Young Adult</subject><ispartof>PloS one, 2018-04, Vol.13 (4), p.e0195125-e0195125</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 von Tscharner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 von Tscharner et al 2018 von Tscharner et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-aecbcd9027ae9fc373bdbf1171dd5990983fed5aaa2479fdb71501bc56ca3b1e3</citedby><cites>FETCH-LOGICAL-c692t-aecbcd9027ae9fc373bdbf1171dd5990983fed5aaa2479fdb71501bc56ca3b1e3</cites><orcidid>0000-0002-5779-6244</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5906018/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5906018/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2104,2930,23873,27931,27932,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29668731$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Jan, Yih-Kuen</contributor><creatorcontrib>von Tscharner, Vinzenz</creatorcontrib><creatorcontrib>Ullrich, Martin</creatorcontrib><creatorcontrib>Mohr, Maurice</creatorcontrib><creatorcontrib>Comaduran Marquez, Daniel</creatorcontrib><creatorcontrib>Nigg, Benno M</creatorcontrib><title>A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
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.
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.
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.</description><subject>Adult</subject><subject>Analysis</subject><subject>Athletes</subject><subject>Biology and Life Sciences</subject><subject>Cluster Analysis</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Diabetes</subject><subject>Electromyography</subject><subject>Electrophysiology</subject><subject>EMG</subject><subject>Engineering and Technology</subject><subject>Frequencies</subject><subject>Frequency analysis</subject><subject>Human performance</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Kinesiology</subject><subject>Laboratories</subject><subject>Leg muscles</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Muscle contraction</subject><subject>Muscle Contraction - physiology</subject><subject>Muscle function</subject><subject>Muscle, Skeletal - physiology</subject><subject>Muscles</subject><subject>Physical Sciences</subject><subject>Physiological aspects</subject><subject>Physiological research</subject><subject>Physiology</subject><subject>Principal components analysis</subject><subject>Research and Analysis Methods</subject><subject>Runners (Sports)</subject><subject>Running</subject><subject>Signal processing</subject><subject>Time-frequency analysis</subject><subject>Walking</subject><subject>Wavelet analysis</subject><subject>Wavelet transforms</subject><subject>Young 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wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns</title><author>von Tscharner, Vinzenz ; Ullrich, Martin ; Mohr, Maurice ; Comaduran Marquez, Daniel ; Nigg, Benno M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-aecbcd9027ae9fc373bdbf1171dd5990983fed5aaa2479fdb71501bc56ca3b1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Analysis</topic><topic>Athletes</topic><topic>Biology and Life Sciences</topic><topic>Cluster Analysis</topic><topic>Correlation</topic><topic>Correlation analysis</topic><topic>Diabetes</topic><topic>Electromyography</topic><topic>Electrophysiology</topic><topic>EMG</topic><topic>Engineering and Technology</topic><topic>Frequencies</topic><topic>Frequency analysis</topic><topic>Human 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Martin</au><au>Mohr, Maurice</au><au>Comaduran Marquez, Daniel</au><au>Nigg, Benno M</au><au>Jan, Yih-Kuen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-04-18</date><risdate>2018</risdate><volume>13</volume><issue>4</issue><spage>e0195125</spage><epage>e0195125</epage><pages>e0195125-e0195125</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.
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.
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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29668731</pmid><doi>10.1371/journal.pone.0195125</doi><tpages>e0195125</tpages><orcidid>https://orcid.org/0000-0002-5779-6244</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Analysis Athletes Biology and Life Sciences Cluster Analysis Correlation Correlation analysis Diabetes Electromyography Electrophysiology EMG Engineering and Technology Frequencies Frequency analysis Human performance Humans Hypotheses Kinesiology Laboratories Leg muscles Male Medicine and Health Sciences Muscle contraction Muscle Contraction - physiology Muscle function Muscle, Skeletal - physiology Muscles Physical Sciences Physiological aspects Physiological research Physiology Principal components analysis Research and Analysis Methods Runners (Sports) Running Signal processing Time-frequency analysis Walking Wavelet analysis Wavelet transforms Young Adult |
| title | A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-04T03%3A50%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20wavelet%20based%20time%20frequency%20analysis%20of%20electromyograms%20to%20group%20steps%20of%20runners%20into%20clusters%20that%20contain%20similar%20muscle%20activation%20patterns&rft.jtitle=PloS%20one&rft.au=von%20Tscharner,%20Vinzenz&rft.date=2018-04-18&rft.volume=13&rft.issue=4&rft.spage=e0195125&rft.epage=e0195125&rft.pages=e0195125-e0195125&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0195125&rft_dat=%3Cgale_plos_%3EA535182847%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2027036342&rft_id=info:pmid/29668731&rft_galeid=A535182847&rft_doaj_id=oai_doaj_org_article_bab9f97f86b343c99719130ee602ea0d&rfr_iscdi=true |