Estimating Oxygen Uptake During Nonsteady-State Activities and Transitions Using Wearable Sensors
In this paper, we present a method to estimate oxygen uptake (VO 2 ) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state VO 2 . Subsequently, we propose and compare activity-specific linear functions...
Saved in:
Published in | IEEE journal of biomedical and health informatics Vol. 20; no. 2; pp. 469 - 475 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | In this paper, we present a method to estimate oxygen uptake (VO 2 ) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state VO 2 . Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from 22 participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running), including many transitions between intensities, thus resulting in nonsteady-state VO 2 . Indirect calorimetry was used in parallel to obtain VO 2 reference. VO 2 estimation error during transitions between sedentary, household and walking activities could be reduced by 16% on average using the proposed approach, compared to state of the art methods. |
---|---|
AbstractList | In this paper, we present a method to estimate oxygen uptake ( VO2) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state VO2. Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from 22 participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running), including many transitions between intensities, thus resulting in nonsteady-state VO2. Indirect calorimetry was used in parallel to obtain VO2 reference. VO2 estimation error during transitions between sedentary, household and walking activities could be reduced by 16% on average using the proposed approach, compared to state of the art methods. In this paper, we present a method to estimate oxygen uptake ([Formula Omitted]) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state [Formula Omitted]. Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from [Formula Omitted] participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running ), including many transitions between intensities, thus resulting in nonsteady-state [Formula Omitted]. Indirect calorimetry was used in parallel to obtain [Formula Omitted] reference. [Formula Omitted] estimation error during transitions between sedentary , household and walking activities could be reduced by [Formula Omitted] on average using the proposed approach, compared to state of the art methods. In this paper, we present a method to estimate oxygen uptake ($V{\rm O_2}$) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state $V{\rm O_2}$. Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from $22$ participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running), including many transitions between intensities, thus resulting in nonsteady-state $V{\rm O_2}$. Indirect calorimetry was used in parallel to obtain $V{\rm O_2}$ reference. $V{\rm O_2}$ estimation error during transitions between sedentary, household and walking activities could be reduced by $16\%$ on average using the proposed approach, compared to state of the art methods. In this paper, we present a method to estimate oxygen uptake (VO 2 ) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state VO 2 . Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from 22 participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running), including many transitions between intensities, thus resulting in nonsteady-state VO 2 . Indirect calorimetry was used in parallel to obtain VO 2 reference. VO 2 estimation error during transitions between sedentary, household and walking activities could be reduced by 16% on average using the proposed approach, compared to state of the art methods. |
Author | Amft, Oliver Penders, Julien Altini, Marco |
Author_xml | – sequence: 1 givenname: Marco surname: Altini fullname: Altini, Marco email: marco.altini@imec-nl.nl organization: Bloom Technol., Diepenbeek, Belgium – sequence: 2 givenname: Julien surname: Penders fullname: Penders, Julien email: julien.penders@imec-nl.nl organization: Holst Centre, imec, Eindhoven, Netherlands – sequence: 3 givenname: Oliver surname: Amft fullname: Amft, Oliver email: amft@ieee.org organization: Univ. of Passau, Passau, Germany |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25594986$$D View this record in MEDLINE/PubMed |
BookMark | eNqNkctOwzAQRS0E4lH4AISELLFhk-JH4thL3i2qYAEVy8hxJijQOsV2EP17HLWwYAPe2L5z7mhGdw9t2tYCQoeUDCkl6uzuYjQeMkKzIeOKpIpvoF1GhUwYI3Lz-01VuoMOvH8l8cgoKbGNdliWqVRJsYv0tQ_NXIfGvuCHz-ULWDxdBP0G-KpzvXjfWh9AV8vkMegA-NyE5qMJDXisbYWfnLY-fiOFp743PIN2upwBfgTrW-f30VatZx4O1vcATW-uny5HyeThdnx5PklMSrOQiJLUtWZEZylXJa8zUyommMxVyquoZYLGqmFGcMIrLQijjBluqtroMtOSD9Dpqu_Cte8d-FDMG29gNtMW2s4XNG5PqFSU_43miqicM6n-geZMcqG4iOjJL_S17ZyNO0dKRiwO3I9JV5RxrfcO6mLhYgBuWVBS9MEWfbBFH2yxDjZ6jtedu3IO1Y_jO8YIHK2ABgB-yjkhQqSEfwFv6Kbn |
CODEN | IJBHA9 |
CitedBy_id | crossref_primary_10_1152_japplphysiol_00299_2017 crossref_primary_10_1109_JBHI_2017_2734803 crossref_primary_10_1049_iet_cvi_2017_0112 crossref_primary_10_3389_fphys_2024_1358785 crossref_primary_10_1007_s11768_023_00188_1 crossref_primary_10_1152_japplphysiol_00148_2023 crossref_primary_10_1038_s41746_021_00531_3 crossref_primary_10_1038_srep45738 crossref_primary_10_31686_ijier_vol7_iss1_1293 crossref_primary_10_1371_journal_pone_0282398 crossref_primary_10_1152_japplphysiol_00760_2017 crossref_primary_10_3389_fphys_2022_897412 crossref_primary_10_3390_s17071698 |
Cites_doi | 10.1152/japplphysiol.01212.2003 10.1016/j.apergo.2007.09.001 10.1088/0967-3334/33/11/1811 10.2165/00007256-200333140-00002 10.2991/cse.2013.24 10.1109/TITB.2011.2165320 10.1186/1743-7075-2-14 10.1152/jappl.1997.83.1.153 10.1109/WCICA.2010.5555072 10.1152/japplphysiol.00150.2009 10.1007/s10439-007-9362-2 10.1249/01.mss.0000177742.12931.50 10.1111/1469-8986.3740543 10.1152/jappl.1967.22.1.71 10.1682/JRRD.2007.11.0197 10.1109/TBME.2013.2284069 10.1007/s11517-009-0534-0 10.1109/BSN.2013.6575500 10.4108/icst.pervasivehealth.2013.252069 10.1113/jphysiol.1994.sp020365 |
ContentType | Journal Article |
Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 |
Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 |
DBID | 97E RIA RIE CGR CUY CVF ECM EIF NPM AAYXX CITATION 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 K9. KR7 L7M L~C L~D NAPCQ P64 7X8 |
DOI | 10.1109/JBHI.2015.2390493 |
DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library Online Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) CrossRef Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic Materials Research Database Engineering Research Database Technology Research Database MEDLINE |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE Electronic Library Online url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 2168-2208 |
EndPage | 475 |
ExternalDocumentID | 4048075491 10_1109_JBHI_2015_2390493 25594986 7006640 |
Genre | orig-research Journal Article |
GroupedDBID | 0R~ 4.4 6IF 6IH 6IK 6IL 97E AAJGR AASAJ ABQJQ ABVLG ACIWK ACPRK ADZIZ AENEX AFRAH AKJIK ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RIG RNS CGR CUY CVF ECM EIF NPM AAYXX AGSQL CITATION 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 K9. KR7 L7M L~C L~D NAPCQ P64 7X8 |
ID | FETCH-LOGICAL-c415t-6b0ffa20a5439b3f5cb926287943d543561a20c2c6303da602122c3cdfcab5a83 |
IEDL.DBID | RIE |
ISSN | 2168-2194 |
IngestDate | Wed Dec 04 07:07:38 EST 2024 Wed Dec 04 02:02:38 EST 2024 Wed Dec 04 03:32:36 EST 2024 Thu Oct 10 16:04:02 EDT 2024 Fri Dec 06 04:18:20 EST 2024 Sat Sep 28 08:06:13 EDT 2024 Wed Jun 26 19:22:21 EDT 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 2 |
Keywords | Accelerometers VO2 heart rate nonsteady-state energy expenditure |
Language | English |
License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c415t-6b0ffa20a5439b3f5cb926287943d543561a20c2c6303da602122c3cdfcab5a83 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
PMID | 25594986 |
PQID | 1787281228 |
PQPubID | 85417 |
PageCount | 7 |
ParticipantIDs | ieee_primary_7006640 proquest_miscellaneous_1772836936 pubmed_primary_25594986 proquest_miscellaneous_1816018913 proquest_journals_1787281228 crossref_primary_10_1109_JBHI_2015_2390493 proquest_miscellaneous_1790973289 |
PublicationCentury | 2000 |
PublicationDate | 2016-March 2016-Mar 2016-3-00 20160301 |
PublicationDateYYYYMMDD | 2016-03-01 |
PublicationDate_xml | – month: 03 year: 2016 text: 2016-March |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: Piscataway |
PublicationTitle | IEEE journal of biomedical and health informatics |
PublicationTitleAbbrev | JBHI |
PublicationTitleAlternate | IEEE J Biomed Health Inform |
PublicationYear | 2016 |
Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
References | ref13 ref12 pulkkinen (ref14) 2004; 36 ref15 wasserman (ref5) 1967; 22 tapia (ref3) 2008 ref20 ref11 ref22 ref10 ref21 ref2 ref1 ref17 ref16 ref19 ref18 ref8 ref7 ref9 ref4 short (ref6) 1997; 83 |
References_xml | – ident: ref10 doi: 10.1152/japplphysiol.01212.2003 – ident: ref13 doi: 10.1016/j.apergo.2007.09.001 – ident: ref11 doi: 10.1088/0967-3334/33/11/1811 – ident: ref17 doi: 10.2165/00007256-200333140-00002 – ident: ref16 doi: 10.2991/cse.2013.24 – ident: ref19 doi: 10.1109/TITB.2011.2165320 – ident: ref4 doi: 10.1186/1743-7075-2-14 – volume: 83 start-page: 153 year: 1997 ident: ref6 article-title: Excess postexercise oxygen consumption and recovery rate in trained and untrained subjects publication-title: J Appl Physiol doi: 10.1152/jappl.1997.83.1.153 contributor: fullname: short – ident: ref20 doi: 10.1109/WCICA.2010.5555072 – ident: ref2 doi: 10.1152/japplphysiol.00150.2009 – ident: ref7 doi: 10.1007/s10439-007-9362-2 – ident: ref15 doi: 10.1249/01.mss.0000177742.12931.50 – year: 2008 ident: ref3 article-title: Using machine learning for real-time activity recognition and estimation of energy expenditure contributor: fullname: tapia – ident: ref21 doi: 10.1111/1469-8986.3740543 – volume: 22 start-page: 71 year: 1967 ident: ref5 article-title: Interaction of physiological mechanisms during exercise publication-title: J Appl Physiol doi: 10.1152/jappl.1967.22.1.71 contributor: fullname: wasserman – ident: ref9 doi: 10.1682/JRRD.2007.11.0197 – volume: 36 year: 2004 ident: ref14 article-title: On-and off dynamics and respiration rate enhance the accuracy of heart rate based vo2 estimation publication-title: Proc ACSM Congr contributor: fullname: pulkkinen – ident: ref22 doi: 10.1109/TBME.2013.2284069 – ident: ref8 doi: 10.1007/s11517-009-0534-0 – ident: ref18 doi: 10.1109/BSN.2013.6575500 – ident: ref1 doi: 10.4108/icst.pervasivehealth.2013.252069 – ident: ref12 doi: 10.1113/jphysiol.1994.sp020365 |
SSID | ssj0000816896 |
Score | 2.2721202 |
Snippet | In this paper, we present a method to estimate oxygen uptake (VO 2 ) during daily life activities and transitions between them. First, we automatically locate... In this paper, we present a method to estimate oxygen uptake ( VO2) during daily life activities and transitions between them. First, we automatically locate... In this paper, we present a method to estimate oxygen uptake ([Formula Omitted]) during daily life activities and transitions between them. First, we... In this paper, we present a method to estimate oxygen uptake ($V{\rm O_2}$) during daily life activities and transitions between them. First, we automatically... |
SourceID | proquest crossref pubmed ieee |
SourceType | Aggregation Database Index Database Publisher |
StartPage | 469 |
SubjectTerms | Accelerometers Accelerometry - methods Adult Calorimetry Data models Energy Expenditure Energy Metabolism - physiology Estimating Estimation Female Heart Rate Households Humans Legged locomotion Logistics Male Mathematical models Monitoring, Ambulatory - methods Non-Steady-State Oxygen - metabolism Oxygen consumption Oxygen Consumption - physiology Predictive models Sensors Signal Processing, Computer-Assisted Steady-state Uptakes V O2 Walking Walking - physiology |
Title | Estimating Oxygen Uptake During Nonsteady-State Activities and Transitions Using Wearable Sensors |
URI | https://ieeexplore.ieee.org/document/7006640 https://www.ncbi.nlm.nih.gov/pubmed/25594986 https://www.proquest.com/docview/1787281228 https://search.proquest.com/docview/1772836936 https://search.proquest.com/docview/1790973289 https://search.proquest.com/docview/1816018913 |
Volume | 20 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8QwEB50D-LF96O-iOBJ7Nqmabs5-mQVVg-66K0kaepB6Ip2wfXXO5N2i4iKt9IOJc3MdL7JvAAOhI0CrqXyU3d0o4T2pQmNT31cipjr2LqI6eAm6Q_F9WP8OANHbS2MtdYln9kuXbpYfj4yYzoqO07JQAp00GdTmdS1Wu15ihsg4cZxcbzwURFFE8QMA3l8fdq_ojyuuMvRyReSxucQmBaSiqi_WCQ3YuV3tOmszuUiDKbrrZNNnrvjSnfNx7dWjv_9oCVYaOAnO6nlZRlmbLkCc4MmwL4K6gJVnkBs-cRu3ycoXWz4Uqlny85dPSO7ITyJcjHxHUxlJ8aNn0B_m6kyZ8701VlgzGUjsAdUJSrPYnfoMI9e39ZgeHlxf9b3mykMvkHjXvmJDopC8UDFiF10VMRGU4_BHnWWy_EeAjB8arhJ0BrmKqGe8dxEJi-M0rHqRevQKUel3QTGQ1GIVFlKGhRWaPSegiLFn0xe4GtU5MHhlBPZS91sI3NOSiAz4mBGHMwaDnqwShvaEjZ76cHOlHdZo45vWYi_JY5Qhvc82G8foyJRdESVdjQmGiSJEhklf9HIur2R_IMGBTEIKfrrwUYtO-0apyK39fPat2EevzCpU9x2oFO9ju0uYp5K7zlh_wT-efmd |
link.rule.ids | 314,780,784,796,27924,27925,54758 |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9NAEB5VQQIuvArF0MIicUJ1aq_XTvbYRyKnNOFAInKzdtfrHiLZUeJIlF_PzNqxEKIVN8seWeudGc83Oy-Az8JGAddS-QN3dKOE9qUJjU99XIqY69i6iOl0lqQLcb2Mlwdw2tXCWGtd8pnt06WL5eeV2dFR2dmADKRAB_1RLBDnNtVa3YmKGyHhBnJxvPBRFUUbxgwDeXZ9kU4okyvuc3TzhaQBOgSnhaQy6j9skhuycj_edHZn_Bym-xU36Sar_q7WffPrr2aO__tJL-BZC0DZeSMxL-HAlq_g8bQNsR-CGqHSE4wtb9m3n3coX2yxrtXKsitX0chmhChRMu58B1TZuXEDKNDjZqrMmTN-TR4Yc_kI7AcqExVose_oMleb7WtYjEfzy9Rv5zD4Bs177Sc6KArFAxUjetFRERtNXQaH1Fsux3sIwfCp4SZBe5irhLrGcxOZvDBKx2oYvYFeWZX2LTAeikIMlKW0QWGFRv8pKAb4m8kLfI2KPPiy50S2btptZM5NCWRGHMyIg1nLQQ8OaUM7wnYvPTje8y5rFXKbhfhj4ghm-NCDT91jVCWKj6jSVjuiQZIokVHyEI1sGhzJB2hQEIOQ4r8eHDWy061xL3Lv_r32j_AknU9vspvJ7Ot7eIpfmzQJb8fQqzc7e4IIqNYfnOD_Bkcb_PA |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+Oxygen+Uptake+During+Nonsteady-State+Activities+and+Transitions+Using+Wearable+Sensors&rft.jtitle=IEEE+journal+of+biomedical+and+health+informatics&rft.au=Altini%2C+Marco&rft.au=Penders%2C+Julien&rft.au=Amft%2C+Oliver&rft.date=2016-03-01&rft.issn=2168-2194&rft.eissn=2168-2208&rft.volume=20&rft.issue=2&rft.spage=469&rft.epage=475&rft_id=info:doi/10.1109%2FJBHI.2015.2390493&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2194&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2194&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2194&client=summon |