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Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports
This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds)....
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Published in: | Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (7), p.2583 |
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description | This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (
= 0.777-0.778) with %HRR; while other combinations were negligible to moderate (
= 0.065-0.451). For basketball, both AIMs at the shank had stronger correlations (
= 0.604-0.628) with %HRR than at wrist (
= 0.536-0.603) and trunk (
= 0.403-0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (
= 0.782-0.793) with %HRR than those at wrist (
= 0.587-0.621) and MAD at trunk (
= 0.608) and trunk (
= 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination. |
doi_str_mv | 10.3390/s22072583 |
format | article |
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= 0.777-0.778) with %HRR; while other combinations were negligible to moderate (
= 0.065-0.451). For basketball, both AIMs at the shank had stronger correlations (
= 0.604-0.628) with %HRR than at wrist (
= 0.536-0.603) and trunk (
= 0.403-0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (
= 0.782-0.793) with %HRR than those at wrist (
= 0.587-0.621) and MAD at trunk (
= 0.608) and trunk (
= 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s22072583</identifier><identifier>PMID: 35408199</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Acceleration ; Accelerometers ; Adult ; Badminton ; Basketball ; Benchmarking ; Correlation ; Data collection ; Exercise ; Exercise intensity ; Fitness equipment ; Heart Rate ; Humans ; Locomotion ; Physical fitness ; racquet sports ; Racquet Sports - physiology ; Running ; Running - physiology ; Sensors ; team sports ; Wearable computers ; wearable electronic devices ; Wrist ; Young Adult</subject><ispartof>Sensors (Basel, Switzerland), 2022-03, Vol.22 (7), p.2583</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-b293793570cf175dc9ebc7aa30df8c941fdfc9e83cfdc95f21b5074d6d64c2e53</citedby><cites>FETCH-LOGICAL-c399t-b293793570cf175dc9ebc7aa30df8c941fdfc9e83cfdc95f21b5074d6d64c2e53</cites><orcidid>0000-0002-7463-8301 ; 0000-0002-4742-587X ; 0000-0003-1744-7020</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2649048608/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2649048608?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35408199$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Wei-Han</creatorcontrib><creatorcontrib>Chiang, Chun-Wei</creatorcontrib><creatorcontrib>Fiolo, Nicholas J</creatorcontrib><creatorcontrib>Fuchs, Philip X</creatorcontrib><creatorcontrib>Shiang, Tzyy-Yuang</creatorcontrib><title>Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports</title><title>Sensors (Basel, Switzerland)</title><addtitle>Sensors (Basel)</addtitle><description>This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (
= 0.777-0.778) with %HRR; while other combinations were negligible to moderate (
= 0.065-0.451). For basketball, both AIMs at the shank had stronger correlations (
= 0.604-0.628) with %HRR than at wrist (
= 0.536-0.603) and trunk (
= 0.403-0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (
= 0.782-0.793) with %HRR than those at wrist (
= 0.587-0.621) and MAD at trunk (
= 0.608) and trunk (
= 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.</description><subject>Acceleration</subject><subject>Accelerometers</subject><subject>Adult</subject><subject>Badminton</subject><subject>Basketball</subject><subject>Benchmarking</subject><subject>Correlation</subject><subject>Data collection</subject><subject>Exercise</subject><subject>Exercise intensity</subject><subject>Fitness equipment</subject><subject>Heart Rate</subject><subject>Humans</subject><subject>Locomotion</subject><subject>Physical fitness</subject><subject>racquet sports</subject><subject>Racquet Sports - physiology</subject><subject>Running</subject><subject>Running - physiology</subject><subject>Sensors</subject><subject>team sports</subject><subject>Wearable computers</subject><subject>wearable electronic devices</subject><subject>Wrist</subject><subject>Young Adult</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkstuEzEUhkcIREthwQsgS2xgMeDbzNgbpBIKRGoFUsvacuzj4mhiB9uDyEPwzphJiVJWtv7znf9cdJrmOcFvGJP4baYUD7QT7EFzSjjlrajCw6P_SfMk5zXGlDEmHjcnrONYEClPm99LC3pEi7hZ-aCLjyGj6NC5MTBCmoX2vc5g0TIUCNmXHbqCkrzJSAeLrqsWE_oaa2ROLhFdxeBLFS9-QTI-w1HqFCwk9ME7BwlCQTe7LcwFr7cxlfy0eeT0mOHZ3XvWfPt4cbP43F5--bRcnF-2hklZ2hWVbJCsG7BxZOiskbAyg9YMWyeM5MRZVzXBjKuxzlGy6vDAbW97bih07KxZ7n1t1Gu1TX6j005F7dUsxHSrdCrejKAMNcxasGLoOWdAZC1EnNEgKCaY4ur1bu-1nVYbsKaOlfR4z_R-JPjv6jb-VBJjxntZDV7dGaT4Y4Jc1Mbnuv5RB4hTVrTnspOEM1LRl_-h6zilUFc1U5iLHotKvd5TJsWcE7hDMwSrvwejDgdT2RfH3R_IfxfC_gCfSr4n</recordid><startdate>20220328</startdate><enddate>20220328</enddate><creator>Chen, Wei-Han</creator><creator>Chiang, Chun-Wei</creator><creator>Fiolo, Nicholas J</creator><creator>Fuchs, Philip X</creator><creator>Shiang, Tzyy-Yuang</creator><general>MDPI AG</general><general>MDPI</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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-7463-8301</orcidid><orcidid>https://orcid.org/0000-0002-4742-587X</orcidid><orcidid>https://orcid.org/0000-0003-1744-7020</orcidid></search><sort><creationdate>20220328</creationdate><title>Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports</title><author>Chen, Wei-Han ; Chiang, Chun-Wei ; Fiolo, Nicholas J ; Fuchs, Philip X ; Shiang, Tzyy-Yuang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-b293793570cf175dc9ebc7aa30df8c941fdfc9e83cfdc95f21b5074d6d64c2e53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Acceleration</topic><topic>Accelerometers</topic><topic>Adult</topic><topic>Badminton</topic><topic>Basketball</topic><topic>Benchmarking</topic><topic>Correlation</topic><topic>Data collection</topic><topic>Exercise</topic><topic>Exercise intensity</topic><topic>Fitness equipment</topic><topic>Heart Rate</topic><topic>Humans</topic><topic>Locomotion</topic><topic>Physical fitness</topic><topic>racquet sports</topic><topic>Racquet Sports - physiology</topic><topic>Running</topic><topic>Running - physiology</topic><topic>Sensors</topic><topic>team sports</topic><topic>Wearable computers</topic><topic>wearable electronic devices</topic><topic>Wrist</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Wei-Han</creatorcontrib><creatorcontrib>Chiang, Chun-Wei</creatorcontrib><creatorcontrib>Fiolo, Nicholas J</creatorcontrib><creatorcontrib>Fuchs, Philip X</creatorcontrib><creatorcontrib>Shiang, Tzyy-Yuang</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>ProQuest_Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest - Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Sensors (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Wei-Han</au><au>Chiang, Chun-Wei</au><au>Fiolo, Nicholas J</au><au>Fuchs, Philip X</au><au>Shiang, Tzyy-Yuang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports</atitle><jtitle>Sensors (Basel, Switzerland)</jtitle><addtitle>Sensors (Basel)</addtitle><date>2022-03-28</date><risdate>2022</risdate><volume>22</volume><issue>7</issue><spage>2583</spage><pages>2583-</pages><issn>1424-8220</issn><eissn>1424-8220</eissn><abstract>This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (
= 0.777-0.778) with %HRR; while other combinations were negligible to moderate (
= 0.065-0.451). For basketball, both AIMs at the shank had stronger correlations (
= 0.604-0.628) with %HRR than at wrist (
= 0.536-0.603) and trunk (
= 0.403-0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (
= 0.782-0.793) with %HRR than those at wrist (
= 0.587-0.621) and MAD at trunk (
= 0.608) and trunk (
= 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35408199</pmid><doi>10.3390/s22072583</doi><orcidid>https://orcid.org/0000-0002-7463-8301</orcidid><orcidid>https://orcid.org/0000-0002-4742-587X</orcidid><orcidid>https://orcid.org/0000-0003-1744-7020</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acceleration Accelerometers Adult Badminton Basketball Benchmarking Correlation Data collection Exercise Exercise intensity Fitness equipment Heart Rate Humans Locomotion Physical fitness racquet sports Racquet Sports - physiology Running Running - physiology Sensors team sports Wearable computers wearable electronic devices Wrist Young Adult |
title | Ideal Combinations of Acceleration-Based Intensity Metrics and Sensor Positions to Monitor Exercise Intensity under Different Types of Sports |
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