{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:17:14Z","timestamp":1776399434738,"version":"3.51.2"},"reference-count":35,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873307"],"award-info":[{"award-number":["61873307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N2123004"],"award-info":[{"award-number":["N2123004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:p>\nTraditional approaches to improving basketball players\u2019 shooting skills rely on coaches\u2019 experience in adjusting players\u2019 biomechanical motions. However, such an approach cannot provide specific instructions or facilitate immediate feedback for improvement of the shooting motion. In this article, a method is presented to quantitatively evaluate four key action indicators of shooting basketballs using a machine\u2010learning model based on Bayesian optimization of a light gradient boosting machine (LightGBM). Important motion data for the model are collected by micro\u2010inertial measurement units embedded in a wrist motion sensor and an internet of things (IoT) smart basketball. Basketball shooting motion data are collected from 16 subjects and used for model training and data testing, and four important action indicators that influence the shot quality are selected for quantitative assessment. The LightGBM model is then developed for the regression prediction of the four action indicators of shooting. In the results, it is indicated that for an individual player, the highest correlation scores of the four indexes range from 97.6% to 99.3%. The proposed approach for quantitatively assessing shooting indexes can provide objective and data\u2010based guidance to improve players\u2019 shooting performance. Foreseeably, the prediction model can be embedded into a chip of a wearable device to evaluate the real\u2010time shot quality quantitatively.<\/jats:p>","DOI":"10.1002\/aisy.202300239","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T03:00:05Z","timestamp":1695783605000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Using IoT Smart Basketball and Wristband Motion Data to Quantitatively Evaluate Action Indicators for Basketball Shooting"],"prefix":"10.1002","volume":"5","author":[{"given":"Yuliang","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Control Engineering Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9369-6884","authenticated-orcid":false,"given":"Xiaoai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Control Engineering Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Control Engineering Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"given":"Weishi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Control Engineering Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"given":"Zhiwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Control Engineering Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"given":"Meilun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Control Engineering Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"given":"Wenyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Arts Beijing Technology and Business University  Beijing 100048 China"}]},{"given":"Zhiping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Physical Education Northeastern University at Qinhuangdao  Qinhuangdao Hebei 066000 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9657-5383","authenticated-orcid":false,"given":"Meng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering City University of Hong Kong  Hong Kong SAR 999077 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9616-6213","authenticated-orcid":false,"given":"Wen Jung","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering City University of Hong Kong  Hong Kong SAR 999077 China"}]}],"member":"311","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/24748668.2009.11868464"},{"key":"e_1_2_10_3_1","first-page":"7790","volume":"12","author":"Giblin G.","year":"2016","journal-title":"Sensoria J. 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