{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:51:42Z","timestamp":1778003502059,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006636","name":"Swedish Research Council for Health, Working Life and Welfare","doi-asserted-by":"publisher","award":["COFAS-2"],"award-info":[{"award-number":["COFAS-2"]}],"id":[{"id":"10.13039\/501100006636","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006636","name":"Swedish Research Council for Health, Working Life and Welfare","doi-asserted-by":"publisher","award":["SDU 2020"],"award-info":[{"award-number":["SDU 2020"]}],"id":[{"id":"10.13039\/501100006636","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006356","name":"University of Southern Denmark","doi-asserted-by":"publisher","award":["COFAS-2"],"award-info":[{"award-number":["COFAS-2"]}],"id":[{"id":"10.13039\/501100006356","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006356","name":"University of Southern Denmark","doi-asserted-by":"publisher","award":["SDU 2020"],"award-info":[{"award-number":["SDU 2020"]}],"id":[{"id":"10.13039\/501100006356","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects\u2019 hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data. The acceleration was measured in children (N = 33) performing a standardized activity protocol in their natural environment. The energy expenditure was modelled using Multiple Linear Regression (MLR), stacked long short-term memory (LSTM) networks, and combined convolutional neural networks (CNN) and LSTM. The correlation and mean absolute percentage error (MAPE) were 0.76 and 19.9% for the MLR, 0.882 and 0.879 and 14.22% for the LSTM, and, with the combined LSTM-CNN, the best performance of 0.883 and 13.9% was achieved. The prediction error for vigorous intensities was significantly different (p &lt; 0.01) from those of the other intensity domains: sedentary, light, and moderate. Utilizing the temporal elements of movement significantly improves energy expenditure prediction accuracy compared to other conventional approaches, but the prediction error for vigorous intensities requires further investigation.<\/jats:p>","DOI":"10.3390\/s24082520","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T08:08:12Z","timestamp":1713168492000},"page":"2520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Martin","family":"Vib\u00e6k","sequence":"first","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3180-4365","authenticated-orcid":false,"given":"Abdolrahman","family":"Peimankar","sequence":"additional","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6898-4083","authenticated-orcid":false,"given":"Uffe Kock","family":"Wiil","sequence":"additional","affiliation":[{"name":"SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Arvidsson","sequence":"additional","affiliation":[{"name":"Center for Health and Performance, Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg, 405 30 Gothenburg, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6718-3022","authenticated-orcid":false,"given":"Jan Christian","family":"Br\u00f8nd","sequence":"additional","affiliation":[{"name":"Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,14]]},"reference":[{"key":"ref_1","unstructured":"Lou, D. 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