From big data to rich data: The key features of athlete wheelchair mobility performance
Abstract Quantitative assessment of an athlete׳s individual wheelchair mobility performance is one prerequisite needed to evaluate game performance, improve wheelchair settings and optimize training routines. Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative asse...
Gespeichert in:
| Veröffentlicht in: | Journal of biomechanics 2016-10, Vol.49 (14), p.3340-3346 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3346 |
|---|---|
| container_issue | 14 |
| container_start_page | 3340 |
| container_title | Journal of biomechanics |
| container_volume | 49 |
| creator | van der Slikke, R.M.A Berger, M.A.M Bregman, D.J.J Veeger, H.E.J |
| description | Abstract Quantitative assessment of an athlete׳s individual wheelchair mobility performance is one prerequisite needed to evaluate game performance, improve wheelchair settings and optimize training routines. Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative assessment, providing a large number of kinematic data. The goal of this research was to reduce that large amount of data to a set of key features best describing wheelchair mobility performance in match play and present them in meaningful way for both scientists and athletes. To test the discriminative power, wheelchair mobility characteristics of athletes with different performance levels were compared. The wheelchair kinematics of 29 (inter-)national level athletes were measured during a match using three inertial sensors mounted on the wheelchair. Principal component analysis was used to reduce 22 kinematic outcomes to a set of six outcomes regarding linear and rotational movement; speed and acceleration; average and best performance. In addition, it was explored whether groups of athletes with known performance differences based on their impairment classification also differed with respect to these key outcomes using univariate general linear models. For all six key outcomes classification showed to be a significant factor ( p |
| doi_str_mv | 10.1016/j.jbiomech.2016.08.022 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1855363295</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>1_s2_0_S0021929016309381</els_id><sourcerecordid>4225194411</sourcerecordid><originalsourceid>FETCH-LOGICAL-c631t-962b5754369c26d66b6ca772991f053079a2c05accb5de6f7d48ff543e762e63</originalsourceid><addsrcrecordid>eNqNkk1v1DAQhi0EokvhL1SWuHBJ8EdsxxwQVUUBqVIPXalHy3EmxCFZL3YC2n-P021B6oVebI30zGuNn0HojJKSEirfD-XQ-DCB60uW65LUJWHsGdrQWvGC8Zo8RxtCGC000-QEvUppIISoSumX6IQpSZlWfINuL2OYcOO_49bOFs8BR-_6u-ID3vaAf8ABd2DnJULCocN27keYAf_uAUbXWx_xFBo_-vmA9xC7ECe7c_AavejsmODN_X2Ktpeftxdfi6vrL98uzq8KJzmdCy1ZI5SouNSOyVbKRjqrFNOadkRworRljgjrXCNakJ1qq7rrMg9KMpD8FL07xu5j-LlAms3kk4NxtDsISzK0FoJLzrR4AsoVJ1Ut2FNQIShT1Zr69hE6hCXu8sgrRXk-dJ0peaRcDClF6Mw--snGg6HErD7NYB58mtWnIbXJPnPj2X380kzQ_m17EJiBT0cA8if_8hBNch6ygNZHcLNpg___Gx8fRbjR77yzY3YP6d88JjFDzM26VetSUcmJ5jXlfwBE8sa-</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1831318398</pqid></control><display><type>article</type><title>From big data to rich data: The key features of athlete wheelchair mobility performance</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><source>ProQuest Central UK/Ireland</source><creator>van der Slikke, R.M.A ; Berger, M.A.M ; Bregman, D.J.J ; Veeger, H.E.J</creator><creatorcontrib>van der Slikke, R.M.A ; Berger, M.A.M ; Bregman, D.J.J ; Veeger, H.E.J</creatorcontrib><description>Abstract Quantitative assessment of an athlete׳s individual wheelchair mobility performance is one prerequisite needed to evaluate game performance, improve wheelchair settings and optimize training routines. Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative assessment, providing a large number of kinematic data. The goal of this research was to reduce that large amount of data to a set of key features best describing wheelchair mobility performance in match play and present them in meaningful way for both scientists and athletes. To test the discriminative power, wheelchair mobility characteristics of athletes with different performance levels were compared. The wheelchair kinematics of 29 (inter-)national level athletes were measured during a match using three inertial sensors mounted on the wheelchair. Principal component analysis was used to reduce 22 kinematic outcomes to a set of six outcomes regarding linear and rotational movement; speed and acceleration; average and best performance. In addition, it was explored whether groups of athletes with known performance differences based on their impairment classification also differed with respect to these key outcomes using univariate general linear models. For all six key outcomes classification showed to be a significant factor ( p <0.05). We composed a set of six key kinematic outcomes that accurately describe wheelchair mobility performance in match play. The key kinematic outcomes were displayed in an easy to interpret way, usable for athletes, coaches and scientists. This standardized representation enables comparison of different wheelchair sports regarding wheelchair mobility, but also evaluation at the level of an individual athlete. By this means, the tool could enhance further development of wheelchair sports in general.</description><identifier>ISSN: 0021-9290</identifier><identifier>EISSN: 1873-2380</identifier><identifier>DOI: 10.1016/j.jbiomech.2016.08.022</identifier><identifier>PMID: 27612973</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Acceleration ; Adult ; Assessments ; Athletes ; Athletic Performance - statistics & numerical data ; Basketball ; Biomechanical Phenomena ; Classification ; Female ; Humans ; Inertial Measurement Unit ; Kinematics ; Male ; Mathematical models ; Mechanical Phenomena ; Movement ; Physical Medicine and Rehabilitation ; Principal components analysis ; Rugby ; Scientists ; Statistics as Topic ; Wheelchair basketball ; Wheelchair mobility performance ; Wheelchair sports ; Wheelchairs</subject><ispartof>Journal of biomechanics, 2016-10, Vol.49 (14), p.3340-3346</ispartof><rights>Elsevier Ltd</rights><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c631t-962b5754369c26d66b6ca772991f053079a2c05accb5de6f7d48ff543e762e63</citedby><cites>FETCH-LOGICAL-c631t-962b5754369c26d66b6ca772991f053079a2c05accb5de6f7d48ff543e762e63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1831318398?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976,64364,64366,64368,72218</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27612973$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>van der Slikke, R.M.A</creatorcontrib><creatorcontrib>Berger, M.A.M</creatorcontrib><creatorcontrib>Bregman, D.J.J</creatorcontrib><creatorcontrib>Veeger, H.E.J</creatorcontrib><title>From big data to rich data: The key features of athlete wheelchair mobility performance</title><title>Journal of biomechanics</title><addtitle>J Biomech</addtitle><description>Abstract Quantitative assessment of an athlete׳s individual wheelchair mobility performance is one prerequisite needed to evaluate game performance, improve wheelchair settings and optimize training routines. Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative assessment, providing a large number of kinematic data. The goal of this research was to reduce that large amount of data to a set of key features best describing wheelchair mobility performance in match play and present them in meaningful way for both scientists and athletes. To test the discriminative power, wheelchair mobility characteristics of athletes with different performance levels were compared. The wheelchair kinematics of 29 (inter-)national level athletes were measured during a match using three inertial sensors mounted on the wheelchair. Principal component analysis was used to reduce 22 kinematic outcomes to a set of six outcomes regarding linear and rotational movement; speed and acceleration; average and best performance. In addition, it was explored whether groups of athletes with known performance differences based on their impairment classification also differed with respect to these key outcomes using univariate general linear models. For all six key outcomes classification showed to be a significant factor ( p <0.05). We composed a set of six key kinematic outcomes that accurately describe wheelchair mobility performance in match play. The key kinematic outcomes were displayed in an easy to interpret way, usable for athletes, coaches and scientists. This standardized representation enables comparison of different wheelchair sports regarding wheelchair mobility, but also evaluation at the level of an individual athlete. By this means, the tool could enhance further development of wheelchair sports in general.</description><subject>Acceleration</subject><subject>Adult</subject><subject>Assessments</subject><subject>Athletes</subject><subject>Athletic Performance - statistics & numerical data</subject><subject>Basketball</subject><subject>Biomechanical Phenomena</subject><subject>Classification</subject><subject>Female</subject><subject>Humans</subject><subject>Inertial Measurement Unit</subject><subject>Kinematics</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Mechanical Phenomena</subject><subject>Movement</subject><subject>Physical Medicine and Rehabilitation</subject><subject>Principal components analysis</subject><subject>Rugby</subject><subject>Scientists</subject><subject>Statistics as Topic</subject><subject>Wheelchair basketball</subject><subject>Wheelchair mobility performance</subject><subject>Wheelchair sports</subject><subject>Wheelchairs</subject><issn>0021-9290</issn><issn>1873-2380</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkk1v1DAQhi0EokvhL1SWuHBJ8EdsxxwQVUUBqVIPXalHy3EmxCFZL3YC2n-P021B6oVebI30zGuNn0HojJKSEirfD-XQ-DCB60uW65LUJWHsGdrQWvGC8Zo8RxtCGC000-QEvUppIISoSumX6IQpSZlWfINuL2OYcOO_49bOFs8BR-_6u-ID3vaAf8ABd2DnJULCocN27keYAf_uAUbXWx_xFBo_-vmA9xC7ECe7c_AavejsmODN_X2Ktpeftxdfi6vrL98uzq8KJzmdCy1ZI5SouNSOyVbKRjqrFNOadkRworRljgjrXCNakJ1qq7rrMg9KMpD8FL07xu5j-LlAms3kk4NxtDsISzK0FoJLzrR4AsoVJ1Ut2FNQIShT1Zr69hE6hCXu8sgrRXk-dJ0peaRcDClF6Mw--snGg6HErD7NYB58mtWnIbXJPnPj2X380kzQ_m17EJiBT0cA8if_8hBNch6ygNZHcLNpg___Gx8fRbjR77yzY3YP6d88JjFDzM26VetSUcmJ5jXlfwBE8sa-</recordid><startdate>20161003</startdate><enddate>20161003</enddate><creator>van der Slikke, R.M.A</creator><creator>Berger, M.A.M</creator><creator>Bregman, D.J.J</creator><creator>Veeger, H.E.J</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TB</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>7QO</scope><scope>P64</scope></search><sort><creationdate>20161003</creationdate><title>From big data to rich data: The key features of athlete wheelchair mobility performance</title><author>van der Slikke, R.M.A ; Berger, M.A.M ; Bregman, D.J.J ; Veeger, H.E.J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c631t-962b5754369c26d66b6ca772991f053079a2c05accb5de6f7d48ff543e762e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Acceleration</topic><topic>Adult</topic><topic>Assessments</topic><topic>Athletes</topic><topic>Athletic Performance - statistics & numerical data</topic><topic>Basketball</topic><topic>Biomechanical Phenomena</topic><topic>Classification</topic><topic>Female</topic><topic>Humans</topic><topic>Inertial Measurement Unit</topic><topic>Kinematics</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Mechanical Phenomena</topic><topic>Movement</topic><topic>Physical Medicine and Rehabilitation</topic><topic>Principal components analysis</topic><topic>Rugby</topic><topic>Scientists</topic><topic>Statistics as Topic</topic><topic>Wheelchair basketball</topic><topic>Wheelchair mobility performance</topic><topic>Wheelchair sports</topic><topic>Wheelchairs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>van der Slikke, R.M.A</creatorcontrib><creatorcontrib>Berger, M.A.M</creatorcontrib><creatorcontrib>Bregman, D.J.J</creatorcontrib><creatorcontrib>Veeger, H.E.J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Journal of biomechanics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van der Slikke, R.M.A</au><au>Berger, M.A.M</au><au>Bregman, D.J.J</au><au>Veeger, H.E.J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>From big data to rich data: The key features of athlete wheelchair mobility performance</atitle><jtitle>Journal of biomechanics</jtitle><addtitle>J Biomech</addtitle><date>2016-10-03</date><risdate>2016</risdate><volume>49</volume><issue>14</issue><spage>3340</spage><epage>3346</epage><pages>3340-3346</pages><issn>0021-9290</issn><eissn>1873-2380</eissn><abstract>Abstract Quantitative assessment of an athlete׳s individual wheelchair mobility performance is one prerequisite needed to evaluate game performance, improve wheelchair settings and optimize training routines. Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative assessment, providing a large number of kinematic data. The goal of this research was to reduce that large amount of data to a set of key features best describing wheelchair mobility performance in match play and present them in meaningful way for both scientists and athletes. To test the discriminative power, wheelchair mobility characteristics of athletes with different performance levels were compared. The wheelchair kinematics of 29 (inter-)national level athletes were measured during a match using three inertial sensors mounted on the wheelchair. Principal component analysis was used to reduce 22 kinematic outcomes to a set of six outcomes regarding linear and rotational movement; speed and acceleration; average and best performance. In addition, it was explored whether groups of athletes with known performance differences based on their impairment classification also differed with respect to these key outcomes using univariate general linear models. For all six key outcomes classification showed to be a significant factor ( p <0.05). We composed a set of six key kinematic outcomes that accurately describe wheelchair mobility performance in match play. The key kinematic outcomes were displayed in an easy to interpret way, usable for athletes, coaches and scientists. This standardized representation enables comparison of different wheelchair sports regarding wheelchair mobility, but also evaluation at the level of an individual athlete. By this means, the tool could enhance further development of wheelchair sports in general.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>27612973</pmid><doi>10.1016/j.jbiomech.2016.08.022</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0021-9290 |
| ispartof | Journal of biomechanics, 2016-10, Vol.49 (14), p.3340-3346 |
| issn | 0021-9290 1873-2380 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1855363295 |
| source | MEDLINE; Elsevier ScienceDirect Journals; ProQuest Central UK/Ireland |
| subjects | Acceleration Adult Assessments Athletes Athletic Performance - statistics & numerical data Basketball Biomechanical Phenomena Classification Female Humans Inertial Measurement Unit Kinematics Male Mathematical models Mechanical Phenomena Movement Physical Medicine and Rehabilitation Principal components analysis Rugby Scientists Statistics as Topic Wheelchair basketball Wheelchair mobility performance Wheelchair sports Wheelchairs |
| title | From big data to rich data: The key features of athlete wheelchair mobility performance |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T07%3A12%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=From%20big%20data%20to%20rich%20data:%20The%20key%20features%20of%20athlete%20wheelchair%20mobility%20performance&rft.jtitle=Journal%20of%20biomechanics&rft.au=van%20der%20Slikke,%20R.M.A&rft.date=2016-10-03&rft.volume=49&rft.issue=14&rft.spage=3340&rft.epage=3346&rft.pages=3340-3346&rft.issn=0021-9290&rft.eissn=1873-2380&rft_id=info:doi/10.1016/j.jbiomech.2016.08.022&rft_dat=%3Cproquest_cross%3E4225194411%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1831318398&rft_id=info:pmid/27612973&rft_els_id=1_s2_0_S0021929016309381&rfr_iscdi=true |