Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches
Oxygen consumption ( V ˙ O 2 ) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user’s physical inte...
Saved in:
| Published in | PloS one Vol. 19; no. 9; p. e0303317 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
27.09.2024
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Oxygen consumption (
V
˙
O
2
) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user’s physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of
V
˙
O
2
using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate
V
˙
O
2
for intra-subject estimation. However, estimating
V
˙
O
2
with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject
V
˙
O
2
estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min
−1
×kg
−1
, suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. |
|---|---|
| AbstractList | Oxygen consumption () is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user’s physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate for intra-subject estimation. However, estimating with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min−1×kg−1, suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. Oxygen consumption (V O 2) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user's physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of V O 2 using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate V O 2 for intra-subject estimation. However, estimating V O 2 with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject V O 2 estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 mlxmin.sup.-1 xkg.sup.-1, suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. Oxygen consumption ( V ˙ O 2 ) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user’s physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of V ˙ O 2 using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate V ˙ O 2 for intra-subject estimation. However, estimating V ˙ O 2 with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject V ˙ O 2 estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min −1 ×kg −1 , suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user's physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of [Formula: see text] using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate [Formula: see text] for intra-subject estimation. However, estimating [Formula: see text] with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject [Formula: see text] estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min-1×kg-1, suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. Oxygen consumption () is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user’s physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate for intra-subject estimation. However, estimating with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min −1 ×kg −1 , suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user's physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of [Formula: see text] using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate [Formula: see text] for intra-subject estimation. However, estimating [Formula: see text] with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject [Formula: see text] estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min-1×kg-1, suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy.Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user's physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of [Formula: see text] using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate [Formula: see text] for intra-subject estimation. However, estimating [Formula: see text] with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network architectures were tested in various configurations for inter-subject [Formula: see text] estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was Xception network, which yielded average estimation errors as low as 2.43 ml×min-1×kg-1, suggesting that it could be used by athletes and running enthusiasts for monitoring their oxygen consumption over time to detect changes in their movement economy. |
| Audience | Academic |
| Author | Müller, Philipp Cronin, Neil J. Rauhameri, Anton Pham-Dinh, Khoa Trinh, Huy |
| Author_xml | – sequence: 1 givenname: Philipp orcidid: 0000-0003-4314-7339 surname: Müller fullname: Müller, Philipp – sequence: 2 givenname: Khoa orcidid: 0009-0000-4558-3471 surname: Pham-Dinh fullname: Pham-Dinh, Khoa – sequence: 3 givenname: Huy orcidid: 0000-0003-4652-3870 surname: Trinh fullname: Trinh, Huy – sequence: 4 givenname: Anton orcidid: 0000-0002-7021-7868 surname: Rauhameri fullname: Rauhameri, Anton – sequence: 5 givenname: Neil J. surname: Cronin fullname: Cronin, Neil J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39331617$$D View this record in MEDLINE/PubMed |
| BookMark | eNqN0ttq1UAUBuAgFXvQNxANCKIX2c4hh8llKVULhYKn22ElWcnOdjIT54Dt2zthp9ItvZBczDB888Os_KfJkTYak-QlJRvKK_phZ4LVoDZzPN4QTjin1ZPkhNacZSUj_OjB_jg5dW5HSMFFWT5LjnkddUmrkyRcOj9O4Ec9pKP2FrIUdLds0WYuNDtsfWpu7wbUaWu0C9PsR6MjSE3wnTE23YYJdDrA6NPglpwpKD_OClONwYKKi_9t7M8U5tkaaLfonidPe1AOX6zrWfL94-W3i8_Z9c2nq4vz66wtGPEZKxitSloTpKKrK1q0OSAD6JuCikbUWPIGur6CPK-rumbQQ8cEa_OqobwugZ8lr_e5szJOrhNzklNKSlFyLqJ4twprfgV0Xk6ja1Ep0GjCnlZMFHkV6Zt_6OOBqxpAoRx1b-JQ2yVUngtKeM6oWLI2j6j4dTiNcc7Yj_H84ML7gwvReLz1AwTn5NXXL_9vb34c2rcP7BZB-a0zKiw_2R3CV-vrQzNhJ2cba2Pv5H2VIsj3oLXGOYv9X0KJXBp7Py65NFaujeV_AEif3TQ |
| Cites_doi | 10.1007/s12559-020-09734-4 10.1249/MSS.0b013e318217276e 10.1038/s41746-021-00531-3 10.1021/ac60214a047 10.3390/s19061480 10.1109/IJCNN54540.2023.10191095 10.1371/journal.pone.0229466 10.2478/bhk-2019-0008 10.1519/JSC.0b013e3181a39277 10.1152/japplphysiol.00299.2017 10.1109/CVPR.2017.195 10.1162/neco.1997.9.8.1735 10.1109/CVPR.2016.90 10.1109/CVPR.2017.243 10.3390/s23042249 10.24985/ijass.2012.24.1.8 10.1109/IJCNN.2017.7966039 10.1109/72.279188 |
| ContentType | Journal Article |
| Copyright | Copyright: © 2024 Müller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2024 Public Library of Science 2024 Müller 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. 2024 Müller 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. |
| Copyright_xml | – notice: Copyright: © 2024 Müller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: COPYRIGHT 2024 Public Library of Science – notice: 2024 Müller 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. – notice: 2024 Müller 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. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 |
| DOI | 10.1371/journal.pone.0303317 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context Opposing Viewpoints Gale In Context Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection (ProQuest) ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection ProQuest Biological Science Collection Agricultural Science Database ProQuest Health & Medical Collection Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Agricultural Science Database CrossRef MEDLINE MEDLINE - Academic |
| 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: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 1932-6203 |
| ExternalDocumentID | 3110686338 A810342187 39331617 10_1371_journal_pone_0303317 |
| Genre | Journal Article |
| GeographicLocations | Finland |
| GeographicLocations_xml | – name: Finland |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHMBA ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM ADRAZ CGR CUY CVF ECM EIF IPNFZ NPM PJZUB PPXIY PQGLB RIG BBORY PMFND 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI PRINS RC3 7X8 ESTFP |
| ID | FETCH-LOGICAL-c520t-252176190e18d9715c4ae2aafb518b89e63badf7a4497992afad282c47b1396a3 |
| IEDL.DBID | M48 |
| ISSN | 1932-6203 |
| IngestDate | Sun Dec 29 00:43:20 EST 2024 Fri Jul 11 11:37:05 EDT 2025 Fri Jul 25 10:29:48 EDT 2025 Tue Jun 17 22:05:58 EDT 2025 Tue Jun 10 21:01:55 EDT 2025 Fri Jun 27 05:24:33 EDT 2025 Fri Jun 27 05:24:37 EDT 2025 Thu May 22 21:27:21 EDT 2025 Mon Jul 21 05:58:40 EDT 2025 Tue Jul 01 03:45:20 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| License | Copyright: © 2024 Müller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c520t-252176190e18d9715c4ae2aafb518b89e63badf7a4497992afad282c47b1396a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-4314-7339 0000-0002-7021-7868 0009-0000-4558-3471 0000-0003-4652-3870 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1371/journal.pone.0303317 |
| PMID | 39331617 |
| PQID | 3110686338 |
| PQPubID | 1436336 |
| PageCount | e0303317 |
| ParticipantIDs | plos_journals_3110686338 proquest_miscellaneous_3110728547 proquest_journals_3110686338 gale_infotracmisc_A810342187 gale_infotracacademiconefile_A810342187 gale_incontextgauss_ISR_A810342187 gale_incontextgauss_IOV_A810342187 gale_healthsolutions_A810342187 pubmed_primary_39331617 crossref_primary_10_1371_journal_pone_0303317 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-09-27 |
| PublicationDateYYYYMMDD | 2024-09-27 |
| PublicationDate_xml | – month: 09 year: 2024 text: 2024-09-27 day: 27 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2024 |
| Publisher | Public Library of Science |
| Publisher_xml | – name: Public Library of Science |
| References | J Kang (pone.0303317.ref001) 2020; 13 pone.0303317.ref014 JT Connor (pone.0303317.ref016) 1994; 5 R Amelard (pone.0303317.ref006) 2021; 4 pone.0303317.ref017 pone.0303317.ref018 pone.0303317.ref015 pone.0303317.ref019 P Davidson (pone.0303317.ref008) 2023; 23 PG Montgomery (pone.0303317.ref002) 2009; 23 P Davidson (pone.0303317.ref003) 2019; 19 pone.0303317.ref010 A Savitzky (pone.0303317.ref011) 1964; 36 pone.0303317.ref024 LI Tjelta (pone.0303317.ref025) 2012; 24 pone.0303317.ref022 pone.0303317.ref023 A Borror (pone.0303317.ref007) 2019; 11 T Beltrame (pone.0303317.ref009) 2018; 124 A Zignoli (pone.0303317.ref005) 2020; 15 JR Wicks (pone.0303317.ref004) 2011; 43 S Hochreiter (pone.0303317.ref012) 1997; 9 S Scardapane (pone.0303317.ref013) 2020; 12 pone.0303317.ref020 pone.0303317.ref021 |
| References_xml | – ident: pone.0303317.ref010 – volume: 12 start-page: 954 year: 2020 ident: pone.0303317.ref013 article-title: Why Should We Add Early Exits to Neural Networks? publication-title: Cognitive Computation doi: 10.1007/s12559-020-09734-4 – ident: pone.0303317.ref014 – volume: 43 start-page: 2005 issue: 10 year: 2011 ident: pone.0303317.ref004 article-title: HR index–a simple method for the prediction of oxygen uptake publication-title: Medicine and Science in Sports and Exercise doi: 10.1249/MSS.0b013e318217276e – volume: 13 start-page: 1705 issue: 7 year: 2020 ident: pone.0303317.ref001 article-title: Use of Heart Rate Index to Predict Oxygen Uptake–A Validation Study publication-title: International Journal of Exercise Science – ident: pone.0303317.ref020 – volume: 4 start-page: 1 issue: 1 year: 2021 ident: pone.0303317.ref006 article-title: Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities publication-title: NPJ Digital Medicine doi: 10.1038/s41746-021-00531-3 – volume: 36 start-page: 1627 issue: 8 year: 1964 ident: pone.0303317.ref011 article-title: Smoothing and differentiation of data by simplified least squares procedures publication-title: Analytical Chemistry doi: 10.1021/ac60214a047 – volume: 19 start-page: 1480 issue: 6 year: 2019 ident: pone.0303317.ref003 article-title: Continuous analysis of running mechanics by means of an integrated INS/GPS device publication-title: Sensors doi: 10.3390/s19061480 – ident: pone.0303317.ref024 doi: 10.1109/IJCNN54540.2023.10191095 – volume: 15 start-page: e0229466 issue: 3 year: 2020 ident: pone.0303317.ref005 article-title: Estimating an individual’s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study publication-title: PLoS One doi: 10.1371/journal.pone.0229466 – volume: 11 start-page: 60 issue: 1 year: 2019 ident: pone.0303317.ref007 article-title: Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network publication-title: Biomedical Human Kinetics doi: 10.2478/bhk-2019-0008 – volume: 23 start-page: 1489 issue: 5 year: 2009 ident: pone.0303317.ref002 article-title: Validation of heart rate monitor-based predictions of oxygen uptake and energy expenditure publication-title: The Journal of Strength & Conditioning Research doi: 10.1519/JSC.0b013e3181a39277 – volume: 124 start-page: 473 issue: 2 year: 2018 ident: pone.0303317.ref009 article-title: Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models publication-title: Journal of Applied Physiology doi: 10.1152/japplphysiol.00299.2017 – ident: pone.0303317.ref015 – ident: pone.0303317.ref022 doi: 10.1109/CVPR.2017.195 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: pone.0303317.ref012 article-title: Long Short-Term Memory publication-title: Neural Computation doi: 10.1162/neco.1997.9.8.1735 – ident: pone.0303317.ref018 doi: 10.1109/CVPR.2016.90 – ident: pone.0303317.ref019 doi: 10.1109/CVPR.2017.243 – volume: 23 start-page: 2249 year: 2023 ident: pone.0303317.ref008 article-title: Surrogate modelling for oxygen uptake prediction using LSTM neural network publication-title: Sensors doi: 10.3390/s23042249 – volume: 24 start-page: 8 issue: 1 year: 2012 ident: pone.0303317.ref025 article-title: Relationship between Velocity at Anaerobic Threshold and Factors Affecting Velocity at Anaerobic Threshold in Elite Distance Runners publication-title: International Journal of Applied Sports Sciences doi: 10.24985/ijass.2012.24.1.8 – ident: pone.0303317.ref017 doi: 10.1109/IJCNN.2017.7966039 – ident: pone.0303317.ref023 – ident: pone.0303317.ref021 – volume: 5 start-page: 240 issue: 2 year: 1994 ident: pone.0303317.ref016 article-title: Recurrent Neural Networks and Robust Time Series Prediction publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.279188 |
| SSID | ssj0053866 |
| Score | 2.4582124 |
| Snippet | Oxygen consumption (
V
˙
O
2
) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers... Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable... Oxygen consumption (V O 2) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or... Oxygen consumption () is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or... |
| SourceID | plos proquest gale pubmed crossref |
| SourceType | Open Website Aggregation Database Index Database |
| StartPage | e0303317 |
| SubjectTerms | Accuracy Adult Analyzers Athletes Body mass index Configuration management Consumption Data exchange Estimates Estimation Estimation errors Female Females Gait - physiology Global positioning systems GPS Health aspects Heart rate Heart Rate - physiology Humans Male Measurement Navigation systems Neural networks Neural Networks, Computer Oxygen Oxygen consumption Oxygen Consumption - physiology Performance prediction Portable equipment Respiration Running Running - physiology Velocity Walking Walking - physiology Young Adult |
| SummonAdditionalLinks | – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjR1da9UwNOj1xRdxfm06NYqgPmS7SZukfZIhu05BBXWyt5Im6WUwmuttC_Pf75w0veOCim-FnIb2fOfkfBDySnHv6rl0zEhjWd4YwYzQJQNjZIUzSuoG45Cfv6iT0_zTmTxLAbcupVVOOjEqahcsxsgPM7BTqlBwonq3-sVwahTerqYRGjfJLQ6WBlO6isWHSRODLCuVyuUyzQ8TdQ5WofUHwNxZFseUXZujpJRnq4vQ_d3jjJZncZfcSS4jPRppvENu-PYe2UlC2dE3qXP02_tkOAaBRRe0XdJzjNoyalqHj37NuqHGmAsNl7-BaaiNtZdRYQAADUPvQljTOLOPLs15TzElfkmnjEOKnS_hM9oxb5xOzch994CcLo5_vD9haa4Cs1LMeybAZGP0Yu554UrNpc2NF8Y0teRFXZReZbVxjTZ5jpd-wjTGwcnM5roGf1GZ7CGZtYDDXUyM4sqDD9Z4U-faytLavPSN8uBXyVL4PcIm9FarsX1GFe_QNBw7RvRVSI4qkWOPPEcaVGMR6Eb6qqOCY69CXgDEywiBvStaTI5ZmqHrqo9ff_4H0PdvW0CvE1ATgCLWpIIE-BzsibUFub8FCRJot5Z3kWOmH-qqa16FNycu-vPyi80ybooJb60PwwijsboVdn80ct8GgVkJqALP8_G_N39CbgvwwDC5Reh9MuvXg38KHlRfP4ticgWhBBvE priority: 102 providerName: ProQuest |
| Title | Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39331617 https://www.proquest.com/docview/3110686338 https://www.proquest.com/docview/3110728547 http://dx.doi.org/10.1371/journal.pone.0303317 |
| Volume | 19 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3da9swEBdtCqMvo91Xu7aZNgbbHhRi2ZLshzK6kqwbtBvdMvJmZFkOhWKnsQ3tf987Ww4YGtiLMehk7NOd76fTfRDyUXo2TcYiZVpow4JMc6a5ihgYI8NTLYXK0A95eSUvZsHPuZhvka5nq2Ng-eTWDvtJzVa3o_u7h6-g8KdN1wbldZNGyyK3IxBaH2ziNtkB26Swp8FlsD5XAO2W0iXQbZq5S575sMtH3N-zVe6PPVjeFuVmONqYpekeee7wJD1rBWCfbNn8Bdl3GlvSz66s9JeXpJ6ANiM-zRf0Bl26jOo8xVu7YmWdoEOGFvcPIFHUNImZzd8ECGhRV2lRrGjT0I8u9E1FMV5-QbtwRIplMeE18jaonHaVym35isymk7_nF8w1XWBG8HHFONhzdG2MrRemkfKECbTlWmeJ8MIkjKz0E51mSgcBnghynekUtm0mUAmASan912SQAzsPMGrKkxYAWmZ1EigjImOCyGbSAugSEbeHhHXsjZdtbY24OWBTsCdp2RfjysRuZQ7JO1yDuM0QXatmfBZ6WMjQC4HiQ0OBhS1yjJxZ6Los4x-__v0H0Z_rHtEnR5QVsCJGu2wFeB0smNWjPO5Rgnqa3vABSkz3QWXsA-CSofT9EGZ2UvT08Pv1MD4Uo-FyW9QtjcLUV3j6m1b61gzsZPftxpEjsssBmWHQC1fHZFCtansCyKpKhmRbzRVcw3MPr9PvQ7LzbXL1-3rY-CqGjTI9Amx7JpQ |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3LbtQw0CrLAS6o5dXSQg0CAQe3Gye2kwNCFXTZpQ8kaKvegmM7q0ooWTaJoD_FNzKTOFutBIhLb5E8sZJ5jz0PQp7LwNlsKCzTQhsW5ZozzVXCwBgZbrUUKsdzyKNjOT6NPp6L8xXyq6-FwbTKXie2itqWBs_Id0OwUzKWEFG9nX1nODUKb1f7ERodWxy4yx8QslVvJu-Bvi84H-2fvBszP1WAGcGHNeNgsDB2H7ogtokKhIm041rnmQjiLE6cDDNtc6WjCK-8uM61hbjERCoDb0nqEPa9QW5GIVhyrEwffeg1P-gOKX15XqiCXc8NO7OycDsgTGHYjkW7Mn_eCAxm38rq7x5ua-lGq-SOd1HpXsdTa2TFFXfJmlcCFX3lO1W_vkeafVAQ6PIWU3qBp8SM6sLio5uzqsnwjIeWPy-BSalpaz1bBQUAtGxqW5Zz2s4IpFN9UVNMwZ_SPsORYqdN-Iyiy1OnffNzV90np9eC8QdkUAAO1zERK5AOfL7c6SxSRiTGRInLpQM_TiTcbRDWozedde060vbOTkGY06EvRXKknhwbZBtpkHZFpwtpT_fiAHsjBjFAPGshsFdGgck4U91UVTr5dPYfQF8-LwG99EB5CRQx2hdAwOdgD64lyK0lSJB4s7S8jhzT_1CVXskGvNlz0Z-Xny6WcVNMsCtc2XQwCqtpYfeHHfctEBgmgCrwdB_9e_Ntcmt8cnSYHk6ODzbJbQ7eHybWcLVFBvW8cY_Be6uzJ63IUPL1umX0N3ZjWBg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3LbtQw0CqLhLhULa-2FGoQCDi4u3FiOzkgVLVddSkUBBT1FhzHWVVCyXaTCPprfB0zjrPVSoC49BbJEyuZ99jzIOSZDGyejUTOtNCGRYXmTHOVMDBGhudaClXgOeT7E3l0Gr09E2cr5FdfC4Nplb1OdIo6rwyekQ9DsFMylhBRDQufFvHxYPxmdsFwghTetPbjNDoWObaXPyB8q19PDoDWzzkfH37ZP2J-wgAzgo8axsF4YRw_skGcJyoQJtKWa11kIoizOLEyzHReKB1FeP3FdaFziFFMpDLwnKQOYd8b5KYKVYwyFu8v0ktAj0jpS_VCFQw9Z-zOqtLugmCFoRuRdmUKvUEYzL5X9d-9XWf1xmtk1burdK_jr3WyYss7ZN0rhJq-9F2rX90l7SEoC3R_yyk9xxNjRnWZ46Ods7rN8LyHVj8vgWGpcXWfTlkBAK3aJq-qOXXzAulUnzcU0_GntM92pNh1Ez6j7HLWad8I3db3yOm1YPw-GZSAww1MygqkBf-vsDqLlBGJMVFiC2nBpxMJt5uE9ehNZ13rjtTd3ykIeTr0pUiO1JNjk-wgDdKuAHUh-eleHGCfxCAGiKcOAvtmlMiBU93WdTr58PU_gD5_WgJ64YGKCihitC-GgM_BflxLkNtLkCD9Zml5Azmm_6E6vZITeLPnoj8vP1ks46aYbFfaqu1gFFbWwu4POu5bIDBMAFXg9W79e_MdcgukM303OTl-SG5zcAQxx4arbTJo5q19BI5ckz12EkPJt-sW0d8G51wZ |
| 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+intra-+and+inter-subject+oxygen+consumption+in+outdoor+human+gait+using+multiple+neural+network+approaches&rft.jtitle=PloS+one&rft.au=M%C3%BCller%2C+Philipp&rft.au=Pham-Dinh%2C+Khoa&rft.au=Trinh%2C+Huy&rft.au=Rauhameri%2C+Anton&rft.date=2024-09-27&rft.eissn=1932-6203&rft.volume=19&rft.issue=9&rft.spage=e0303317&rft_id=info:doi/10.1371%2Fjournal.pone.0303317&rft_id=info%3Apmid%2F39331617&rft.externalDocID=39331617 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon |