{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:25:09Z","timestamp":1762777509287,"version":"build-2065373602"},"reference-count":14,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the rapid development of artificial intelligence (AI) and internet of things (IoTs) technologies, it has become a challenge task to assist sports training using intelligent system in physical education. This paper establishes a lightweight assisted sports training quality evaluation system using edge\u2010cloud computing and AI technology to weaken this issue. First, the skeleton of person during sports training is captured by Kinect camera; then, the skeleton is represented as joint angle feature vector; lastly, the joint angle feature vector is sent to cloud server in which an ordinal regression forest is deployed. Compared with complex deep learning model, both skeletal joint angle feature extraction and ordinal regression forest need fewer computing resources, which can adapt limited resources in devices under IoT environment. On the other hand, ordinal regression forest can reflect the ordinal relationship between sports training quality levels to reach lower mean absolute error (MAE). The experiments show the effectiveness of the proposed sports training quality evaluation system.<\/jats:p>","DOI":"10.1002\/itl2.639","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:20:31Z","timestamp":1735258831000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight Auxiliary Sports Training Quality Evaluation Based on Edge\u2010Cloud Collaboration"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8764-4885","authenticated-orcid":false,"given":"Aichen","family":"Li","sequence":"first","affiliation":[{"name":"Jilin Institute of Chemical Technology  Jilin China"}]}],"member":"311","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1846345"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07137-0"},{"key":"e_1_2_6_4_1","doi-asserted-by":"crossref","unstructured":"N.Mishra B. G. M.Habal P. S.Garcia andM. B.Garcia \u201cHarnessing an AI\u2010Driven Analytics Model to Optimize Training and Treatment in Physical Education for Sports Injury Prevention \u201dinProceedings of the 2024 8th International Conference on Education and Multimedia Technology (2024) 309\u2013315.","DOI":"10.1145\/3678726.3678740"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0292557"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3477494"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2024.054895"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2013.03.011"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-023-12128-2"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.1026"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120766"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119644"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055816"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2022.2081745"},{"key":"e_1_2_6_15_1","doi-asserted-by":"crossref","unstructured":"Y.Liu Y.Liu andK.Chan \u201cOrdinal Regression via Manifold Learning \u201dinProceedings of the AAAI Conference on Artificial Intelligence25 (2011) 398\u2013403.","DOI":"10.1609\/aaai.v25i1.7937"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.639","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:20:58Z","timestamp":1762777258000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,26]]},"references-count":14,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/itl2.639"],"URL":"https:\/\/doi.org\/10.1002\/itl2.639","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"type":"print","value":"2476-1508"},{"type":"electronic","value":"2476-1508"}],"subject":[],"published":{"date-parts":[[2024,12,26]]},"assertion":[{"value":"2024-11-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e639"}}