{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T10:57:00Z","timestamp":1776769020852,"version":"3.51.2"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Interreg V program Vlaanderen-Nederland","award":["S60810"],"award-info":[{"award-number":["S60810"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Even though practicing sports has great health benefits, it also entails a risk of developing overuse injuries, which can elicit a negative impact on physical, mental, and financial health. Being able to predict the risk of an overuse injury arising is of widespread interest because this may play a vital role in preventing its occurrence. In this paper, we present a machine learning model trained to predict the occurrence of a lower-limb overuse injury (LLOI). This model was trained and evaluated using data from a three-dimensional accelerometer on the lower back, collected during a Cooper test performed by 161 first-year undergraduate students of a movement science program. In this study, gender-specific models performed better than mixed-gender models. The estimated area under the receiving operating characteristic curve of the best-performing male- and female-specific models, trained according to the presented approach, was, respectively, 0.615 and 0.645. In addition, the best-performing models were achieved by combining statistical and sports-specific features. Overall, the results demonstrated that a machine learning injury prediction model is a promising, yet challenging approach.<\/jats:p>","DOI":"10.3390\/s22082860","type":"journal-article","created":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T05:13:08Z","timestamp":1649481188000},"page":"2860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Impact of Gender and Feature Set on Machine-Learning-Based Prediction of Lower-Limb Overuse Injuries Using a Single Trunk-Mounted Accelerometer"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3577-5709","authenticated-orcid":false,"given":"Sieglinde","family":"Bogaert","sequence":"first","affiliation":[{"name":"Human Movements Biomechanics Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesse","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Leuven.AI, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sam","family":"Van Rossom","sequence":"additional","affiliation":[{"name":"Human Movements Biomechanics Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6158-9483","authenticated-orcid":false,"given":"Benedicte","family":"Vanwanseele","sequence":"additional","affiliation":[{"name":"Human Movements Biomechanics Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1097\/00001504-200503000-00013","article-title":"Exercise and well-being: A review of mental and physical health benefits associated with physical activity","volume":"18","author":"Penedo","year":"2005","journal-title":"Curr. 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