{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:51:41Z","timestamp":1778003501068,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yonsei Signature Research Cluster Project","award":["2021-22-0009"],"award-info":[{"award-number":["2021-22-0009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The limited availability of calorimetry systems for estimating human energy expenditure (EE) while conducting exercise has prompted the development of wearable sensors utilizing readily accessible methods. We designed an energy expenditure estimation method which considers the energy consumed during the exercise, as well as the excess post-exercise oxygen consumption (EPOC) using machine learning algorithms. Thirty-two healthy adults (mean age = 28.2 years; 11 females) participated in 20 min of aerobic exercise sessions (low intensity = 40% of maximal oxygen uptake [VO2 max], high intensity = 70% of VO2 max). The physical characteristics, exercise intensity, and the heart rate data monitored from the beginning of the exercise sessions to where the participants\u2019 metabolic rate returned to an idle state were used in the EE estimation models. Our proposed estimation shows up to 0.976 correlation between estimated energy expenditure and ground truth (root mean square error: 0.624 kcal\/min). In conclusion, our study introduces a highly accurate method for estimating human energy expenditure during exercise using wearable sensors and machine learning. The achieved correlation up to 0.976 with ground truth values underscores its potential for widespread use in fitness, healthcare, and sports performance monitoring.<\/jats:p>","DOI":"10.3390\/s23229235","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:49:47Z","timestamp":1700182187000},"page":"9235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Intelligent Estimation of Exercise Induced Energy Expenditure Including Excess Post-Exercise Oxygen Consumption (EPOC) with Different Exercise Intensity"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6719-7934","authenticated-orcid":false,"given":"Junhyung","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3087-5548","authenticated-orcid":false,"given":"Minsuk","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Sport Industry Studies, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"},{"name":"Frontier Research Institute of Convergence Sports Science, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soljee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyoungwoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9221-0405","authenticated-orcid":false,"given":"Junga","family":"Lee","sequence":"additional","affiliation":[{"name":"Graduate School of Sport Science, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8034-8212","authenticated-orcid":false,"given":"Yoonkyung","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Sport Industry Studies, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7978-4271","authenticated-orcid":false,"given":"Justin Y.","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Sport Industry Studies, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"},{"name":"Frontier Research Institute of Convergence Sports Science, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"},{"name":"Exercise Medicine Center for Diabetes and Cancer Patients (ICONS), 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"683","DOI":"10.2165\/00007256-200333090-00004","article-title":"Estimating Human Energy Expenditure: A Review of Techniques with Particular Reference to Doubly Labelled Water","volume":"33","author":"Ainslie","year":"2003","journal-title":"Sport. 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