{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T19:16:48Z","timestamp":1772219808848,"version":"3.50.1"},"reference-count":25,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["287295"],"award-info":[{"award-number":["287295"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["323472"],"award-info":[{"award-number":["323472"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"\n Oxygen consumption (\n \n \n \n \n \n \n \n \n V<\/mml:mtext>\n \u02d9<\/mml:mo>\n <\/mml:mover>\n O<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ) 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\u2019s physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of\n \n \n \n \n \n \n \n \n V<\/mml:mtext>\n \u02d9<\/mml:mo>\n <\/mml:mover>\n O<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n using neural networks combined with motion features and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate\n \n \n \n \n \n \n \n \n V<\/mml:mtext>\n \u02d9<\/mml:mo>\n <\/mml:mover>\n O<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n for intra-subject estimation. However, estimating\n \n \n \n \n \n \n \n \n V<\/mml:mtext>\n \u02d9<\/mml:mo>\n <\/mml:mover>\n O<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 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\n \n \n \n \n \n \n \n \n V<\/mml:mtext>\n \u02d9<\/mml:mo>\n <\/mml:mover>\n O<\/mml:mtext>\n <\/mml:mrow>\n 2<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n 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\u00d7min\n \u22121<\/jats:sup>\n \u00d7kg\n \u22121<\/jats:sup>\n , 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.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0303317","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T13:25:56Z","timestamp":1727443556000},"page":"e0303317","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4314-7339","authenticated-orcid":true,"given":"Philipp","family":"M\u00fcller","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4558-3471","authenticated-orcid":true,"given":"Khoa","family":"Pham-Dinh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4652-3870","authenticated-orcid":true,"given":"Huy","family":"Trinh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7021-7868","authenticated-orcid":true,"given":"Anton","family":"Rauhameri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neil J.","family":"Cronin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"issue":"7","key":"pone.0303317.ref001","first-page":"1705","article-title":"Use of Heart Rate Index to Predict Oxygen Uptake\u2013A Validation Study","volume":"13","author":"J Kang","year":"2020","journal-title":"International Journal of Exercise Science"},{"issue":"5","key":"pone.0303317.ref002","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1519\/JSC.0b013e3181a39277","article-title":"Validation of heart rate monitor-based predictions of oxygen uptake and energy expenditure","volume":"23","author":"PG Montgomery","year":"2009","journal-title":"The Journal of Strength & Conditioning Research"},{"issue":"6","key":"pone.0303317.ref003","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.3390\/s19061480","article-title":"Continuous analysis of running mechanics by means of an integrated INS\/GPS device","volume":"19","author":"P Davidson","year":"2019","journal-title":"Sensors"},{"issue":"10","key":"pone.0303317.ref004","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1249\/MSS.0b013e318217276e","article-title":"HR index\u2013a simple method for the prediction of oxygen uptake","volume":"43","author":"JR Wicks","year":"2011","journal-title":"Medicine and Science in Sports and Exercise"},{"issue":"3","key":"pone.0303317.ref005","doi-asserted-by":"crossref","first-page":"e0229466","DOI":"10.1371\/journal.pone.0229466","article-title":"Estimating an individual\u2019s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study","volume":"15","author":"A Zignoli","year":"2020","journal-title":"PLoS One"},{"issue":"1","key":"pone.0303317.ref006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-021-00531-3","article-title":"Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities","volume":"4","author":"R Amelard","year":"2021","journal-title":"NPJ Digital Medicine"},{"issue":"1","key":"pone.0303317.ref007","doi-asserted-by":"crossref","first-page":"60","DOI":"10.2478\/bhk-2019-0008","article-title":"Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network","volume":"11","author":"A Borror","year":"2019","journal-title":"Biomedical Human Kinetics"},{"key":"pone.0303317.ref008","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.3390\/s23042249","article-title":"Surrogate modelling for oxygen uptake prediction using LSTM neural network","volume":"23","author":"P Davidson","year":"2023","journal-title":"Sensors"},{"issue":"2","key":"pone.0303317.ref009","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1152\/japplphysiol.00299.2017","article-title":"Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models","volume":"124","author":"T Beltrame","year":"2018","journal-title":"Journal of Applied Physiology"},{"key":"pone.0303317.ref010","unstructured":"WMA Declaration of Helsinki\u2014ethical principles for medical research involving human subjects. 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