Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features

Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metab...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sports engineering 2021-12, Vol.24 (1), Article 9
Hauptverfasser: de Almeida e Bueno, Leonardo, Kwong, Man Ting, Milnthorpe, William R. F., Cheng, Runbei, Bergmann, Jeroen H. M.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page
container_title Sports engineering
container_volume 24
creator de Almeida e Bueno, Leonardo
Kwong, Man Ting
Milnthorpe, William R. F.
Cheng, Runbei
Bergmann, Jeroen H. M.
description Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metabolic cart in estimating rated perceived exertion. Eight adult men volunteered to perform treadmill tests under different conditions. Cardiorespiratory data were collected using a metabolic cart and an instrumented oral-cavity device, as well as their ratings of perceived exertion. Pearson correlation corrected for repeated measurements and stepwise regression analysis were used to observe the relationship between the cardiorespiratory features and the ratings of perceived exertion and determine the proportion of the variance of exertion that could be explained by the measurements. Minute ventilation was found to be the most associated variable to perceived exertion, closely followed by a novel metric called the audio minute volume, which can be collected by the oral-cavity device. A generalised linear model combining minute ventilation, audio minute volume, heart rate and respiration rate accounted for 64% of the variance in perceived exertion, whilst a model with only audio minute volume accounted for 56%. Our study indicates that minute ventilation is key to estimating perceived exertion during indoor running exercises. Audio minute volume was also observed to perform comparably to a lab-based metabolic cart in estimating perceived exertion. This research indicates that mobile techniques offer the potential for real-world data collection of an athlete’s physiological load and estimation of perceived exertion.
doi_str_mv 10.1007/s12283-021-00346-1
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2516889702</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2516889702</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-1285a70450a6d55d432c203e21d20485dfe577cc608208df67cb4ce0d54bab2a3</originalsourceid><addsrcrecordid>eNp9UMtKAzEUDaJgrf6AqwHX0Ztk8uiyFF9QcKNbQybJlJQ6M00yYv_e1BHcubrnHs65j4PQNYFbAiDvEqFUMQyUYABWC0xO0IzUAjAVSp4WzMQCS5D0HF2ktAUggig2Q-_LYdgdQrepxibsx5D7MVXJd-lI5b7yKYcPk301-Gh9-PSu8l8-5tB3VWNSaQuwJrrQR5-GEE3u46FqvcljIS7RWWt2yV_91jl6e7h_XT3h9cvj82q5xpYJljGhihsJNQcjHOeuZtRSYJ4SR6FW3LWeS2mtAEVBuVZI29TWg-N1Yxpq2BzdTHOH2O_HcrTe9mPsykpNORFKLSTQoqKTysY-pehbPcTyXTxoAvqYo55y1CVH_ZOjJsXEJlMq4m7j49_of1zf9oV3QA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2516889702</pqid></control><display><type>article</type><title>Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features</title><source>Springer Nature - Complete Springer Journals</source><creator>de Almeida e Bueno, Leonardo ; Kwong, Man Ting ; Milnthorpe, William R. F. ; Cheng, Runbei ; Bergmann, Jeroen H. M.</creator><creatorcontrib>de Almeida e Bueno, Leonardo ; Kwong, Man Ting ; Milnthorpe, William R. F. ; Cheng, Runbei ; Bergmann, Jeroen H. M.</creatorcontrib><description>Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metabolic cart in estimating rated perceived exertion. Eight adult men volunteered to perform treadmill tests under different conditions. Cardiorespiratory data were collected using a metabolic cart and an instrumented oral-cavity device, as well as their ratings of perceived exertion. Pearson correlation corrected for repeated measurements and stepwise regression analysis were used to observe the relationship between the cardiorespiratory features and the ratings of perceived exertion and determine the proportion of the variance of exertion that could be explained by the measurements. Minute ventilation was found to be the most associated variable to perceived exertion, closely followed by a novel metric called the audio minute volume, which can be collected by the oral-cavity device. A generalised linear model combining minute ventilation, audio minute volume, heart rate and respiration rate accounted for 64% of the variance in perceived exertion, whilst a model with only audio minute volume accounted for 56%. Our study indicates that minute ventilation is key to estimating perceived exertion during indoor running exercises. Audio minute volume was also observed to perform comparably to a lab-based metabolic cart in estimating perceived exertion. This research indicates that mobile techniques offer the potential for real-world data collection of an athlete’s physiological load and estimation of perceived exertion.</description><identifier>ISSN: 1369-7072</identifier><identifier>EISSN: 1460-2687</identifier><identifier>DOI: 10.1007/s12283-021-00346-1</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Biomedical Engineering and Bioengineering ; Data collection ; Engineering ; Engineering Design ; Estimation ; Heart rate ; Materials Science ; Metabolism ; Monitoring ; Original Article ; Ratings ; Regression analysis ; Rehabilitation Medicine ; Sports Medicine ; Theoretical and Applied Mechanics ; Treadmills ; Variance ; Ventilation</subject><ispartof>Sports engineering, 2021-12, Vol.24 (1), Article 9</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-1285a70450a6d55d432c203e21d20485dfe577cc608208df67cb4ce0d54bab2a3</citedby><cites>FETCH-LOGICAL-c363t-1285a70450a6d55d432c203e21d20485dfe577cc608208df67cb4ce0d54bab2a3</cites><orcidid>0000-0001-7306-2630</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12283-021-00346-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12283-021-00346-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>de Almeida e Bueno, Leonardo</creatorcontrib><creatorcontrib>Kwong, Man Ting</creatorcontrib><creatorcontrib>Milnthorpe, William R. F.</creatorcontrib><creatorcontrib>Cheng, Runbei</creatorcontrib><creatorcontrib>Bergmann, Jeroen H. M.</creatorcontrib><title>Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features</title><title>Sports engineering</title><addtitle>Sports Eng</addtitle><description>Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metabolic cart in estimating rated perceived exertion. Eight adult men volunteered to perform treadmill tests under different conditions. Cardiorespiratory data were collected using a metabolic cart and an instrumented oral-cavity device, as well as their ratings of perceived exertion. Pearson correlation corrected for repeated measurements and stepwise regression analysis were used to observe the relationship between the cardiorespiratory features and the ratings of perceived exertion and determine the proportion of the variance of exertion that could be explained by the measurements. Minute ventilation was found to be the most associated variable to perceived exertion, closely followed by a novel metric called the audio minute volume, which can be collected by the oral-cavity device. A generalised linear model combining minute ventilation, audio minute volume, heart rate and respiration rate accounted for 64% of the variance in perceived exertion, whilst a model with only audio minute volume accounted for 56%. Our study indicates that minute ventilation is key to estimating perceived exertion during indoor running exercises. Audio minute volume was also observed to perform comparably to a lab-based metabolic cart in estimating perceived exertion. This research indicates that mobile techniques offer the potential for real-world data collection of an athlete’s physiological load and estimation of perceived exertion.</description><subject>Biomedical Engineering and Bioengineering</subject><subject>Data collection</subject><subject>Engineering</subject><subject>Engineering Design</subject><subject>Estimation</subject><subject>Heart rate</subject><subject>Materials Science</subject><subject>Metabolism</subject><subject>Monitoring</subject><subject>Original Article</subject><subject>Ratings</subject><subject>Regression analysis</subject><subject>Rehabilitation Medicine</subject><subject>Sports Medicine</subject><subject>Theoretical and Applied Mechanics</subject><subject>Treadmills</subject><subject>Variance</subject><subject>Ventilation</subject><issn>1369-7072</issn><issn>1460-2687</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9UMtKAzEUDaJgrf6AqwHX0Ztk8uiyFF9QcKNbQybJlJQ6M00yYv_e1BHcubrnHs65j4PQNYFbAiDvEqFUMQyUYABWC0xO0IzUAjAVSp4WzMQCS5D0HF2ktAUggig2Q-_LYdgdQrepxibsx5D7MVXJd-lI5b7yKYcPk301-Gh9-PSu8l8-5tB3VWNSaQuwJrrQR5-GEE3u46FqvcljIS7RWWt2yV_91jl6e7h_XT3h9cvj82q5xpYJljGhihsJNQcjHOeuZtRSYJ4SR6FW3LWeS2mtAEVBuVZI29TWg-N1Yxpq2BzdTHOH2O_HcrTe9mPsykpNORFKLSTQoqKTysY-pehbPcTyXTxoAvqYo55y1CVH_ZOjJsXEJlMq4m7j49_of1zf9oV3QA</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>de Almeida e Bueno, Leonardo</creator><creator>Kwong, Man Ting</creator><creator>Milnthorpe, William R. F.</creator><creator>Cheng, Runbei</creator><creator>Bergmann, Jeroen H. M.</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TS</scope><orcidid>https://orcid.org/0000-0001-7306-2630</orcidid></search><sort><creationdate>20211201</creationdate><title>Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features</title><author>de Almeida e Bueno, Leonardo ; Kwong, Man Ting ; Milnthorpe, William R. F. ; Cheng, Runbei ; Bergmann, Jeroen H. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-1285a70450a6d55d432c203e21d20485dfe577cc608208df67cb4ce0d54bab2a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Biomedical Engineering and Bioengineering</topic><topic>Data collection</topic><topic>Engineering</topic><topic>Engineering Design</topic><topic>Estimation</topic><topic>Heart rate</topic><topic>Materials Science</topic><topic>Metabolism</topic><topic>Monitoring</topic><topic>Original Article</topic><topic>Ratings</topic><topic>Regression analysis</topic><topic>Rehabilitation Medicine</topic><topic>Sports Medicine</topic><topic>Theoretical and Applied Mechanics</topic><topic>Treadmills</topic><topic>Variance</topic><topic>Ventilation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Almeida e Bueno, Leonardo</creatorcontrib><creatorcontrib>Kwong, Man Ting</creatorcontrib><creatorcontrib>Milnthorpe, William R. F.</creatorcontrib><creatorcontrib>Cheng, Runbei</creatorcontrib><creatorcontrib>Bergmann, Jeroen H. M.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>Physical Education Index</collection><jtitle>Sports engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Almeida e Bueno, Leonardo</au><au>Kwong, Man Ting</au><au>Milnthorpe, William R. F.</au><au>Cheng, Runbei</au><au>Bergmann, Jeroen H. M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features</atitle><jtitle>Sports engineering</jtitle><stitle>Sports Eng</stitle><date>2021-12-01</date><risdate>2021</risdate><volume>24</volume><issue>1</issue><artnum>9</artnum><issn>1369-7072</issn><eissn>1460-2687</eissn><abstract>Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metabolic cart in estimating rated perceived exertion. Eight adult men volunteered to perform treadmill tests under different conditions. Cardiorespiratory data were collected using a metabolic cart and an instrumented oral-cavity device, as well as their ratings of perceived exertion. Pearson correlation corrected for repeated measurements and stepwise regression analysis were used to observe the relationship between the cardiorespiratory features and the ratings of perceived exertion and determine the proportion of the variance of exertion that could be explained by the measurements. Minute ventilation was found to be the most associated variable to perceived exertion, closely followed by a novel metric called the audio minute volume, which can be collected by the oral-cavity device. A generalised linear model combining minute ventilation, audio minute volume, heart rate and respiration rate accounted for 64% of the variance in perceived exertion, whilst a model with only audio minute volume accounted for 56%. Our study indicates that minute ventilation is key to estimating perceived exertion during indoor running exercises. Audio minute volume was also observed to perform comparably to a lab-based metabolic cart in estimating perceived exertion. This research indicates that mobile techniques offer the potential for real-world data collection of an athlete’s physiological load and estimation of perceived exertion.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s12283-021-00346-1</doi><orcidid>https://orcid.org/0000-0001-7306-2630</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1369-7072
ispartof Sports engineering, 2021-12, Vol.24 (1), Article 9
issn 1369-7072
1460-2687
language eng
recordid cdi_proquest_journals_2516889702
source Springer Nature - Complete Springer Journals
subjects Biomedical Engineering and Bioengineering
Data collection
Engineering
Engineering Design
Estimation
Heart rate
Materials Science
Metabolism
Monitoring
Original Article
Ratings
Regression analysis
Rehabilitation Medicine
Sports Medicine
Theoretical and Applied Mechanics
Treadmills
Variance
Ventilation
title Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T04%3A43%3A01IST&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=Applying%20ubiquitous%20sensing%20to%20estimate%20perceived%20exertion%20based%20on%20cardiorespiratory%20features&rft.jtitle=Sports%20engineering&rft.au=de%20Almeida%20e%20Bueno,%20Leonardo&rft.date=2021-12-01&rft.volume=24&rft.issue=1&rft.artnum=9&rft.issn=1369-7072&rft.eissn=1460-2687&rft_id=info:doi/10.1007/s12283-021-00346-1&rft_dat=%3Cproquest_cross%3E2516889702%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=2516889702&rft_id=info:pmid/&rfr_iscdi=true