{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T18:21:25Z","timestamp":1778955685826,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,7,17]]},"abstract":"<jats:p>In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore, discovering the causes of athletes\u2019 injuries in time and preventing them in time is crucial for improving athletes\u2019 skill level and prolonging sports life. Through the functional movement screening (FMS) test, understanding young aerobics athletes\u2019 insufficiency in trunk stability, joint flexibility, muscle extension, and core strength can further help athletes reduce the risk of sports injuries. Therefore, this article proposes a novel sports injury risk model based on big data technology and deep learning, which can effectively predict the risk of sports injury and can play a positive role in improving the quality of athletes\u2019 movements and prolonging their sports life.<\/jats:p>","DOI":"10.1155\/2021\/3370580","type":"journal-article","created":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T20:20:07Z","timestamp":1626553207000},"page":"1-7","source":"Crossref","is-referenced-by-count":5,"title":["Big Data and Deep Learning Model for FMS Score Prediction of Aerobics Athletes"],"prefix":"10.1155","volume":"2021","author":[{"given":"Wenying","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Physical Education and Health, Jiangxi University of Traditional Chinese Medicine, Nanchang 330000, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongqin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Physical Education and Health, Jiangxi University of Traditional Chinese Medicine, Nanchang 330000, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7387-2342","authenticated-orcid":true,"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Physical Education and Health, Jiangxi University of Traditional Chinese Medicine, Nanchang 330000, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.15391\/snsv.2017-1.010"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2021.103925"},{"key":"3","first-page":"614","article-title":"Simulation algorithm of difficulty movements in competitive aerobics","author":"Y. 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