{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T18:51:19Z","timestamp":1767898279055,"version":"3.49.0"},"reference-count":0,"publisher":"Georg Thieme Verlag KG","issue":"04","funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of\n                        China","doi-asserted-by":"crossref","award":["11702175, 31570944"],"award-info":[{"award-number":["11702175, 31570944"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Natural Science Foundation of Guangdong\n                        Province","award":["2016A030310068"],"award-info":[{"award-number":["2016A030310068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J Sports Med"],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Basketball activity classification can help document players\u2019 statistics,\n                    allow coaches, trainers and the medical team to quantitatively supervise\n                    players\u2019 physical exertion and optimize training strategy, and further\n                    help prevent potential injuries. Traditionally, sports activity classification\n                    was done by manual notational, or through multi-camera systems or motion sensing\n                    technology. These methods were often erroneous and limited by space. This study\n                    presents a basketball activity classification model based on Dynamic Time\n                    Warping (DTW) and body kinematic measures. Twenty participants, including 10\n                    experienced players and 10 novice players, were involved in an experimental\n                    study. The experienced and novice players differed in their years of playing\n                    basketball. Four basketball movements, including shooting, passing, dribbling,\n                    and lay-up were classified by kinematic measures. The results indicate that the\n                    proposed model can successfully classify different basketball movements with\n                    high accuracy and efficiency. Specifically, with the resultant acceleration of\n                    the hand, this model can achieve classification precision, recall, and\n                    specificity up to 0.984, 0.983 and 0.994, respectively. Findings from this study\n                    supported the feasibility of using DTW in real-time sports activity\n                    classification and provided insights into the optimal sensor placement for\n                    basketball activity classification applications.<\/jats:p>","DOI":"10.1055\/a-1065-2044","type":"journal-article","created":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T18:39:13Z","timestamp":1579027153000},"page":"255-263","source":"Crossref","is-referenced-by-count":16,"title":["Basketball Activity Classification Based on Upper Body Kinematics and\n                    Dynamic Time Warping"],"prefix":"10.1055","volume":"41","author":[{"given":"Xinyao","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, Shenzhen University,\n                    Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaorong","family":"Mo","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, Shenzhen University,\n                    Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingda","family":"Qu","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, Shenzhen University,\n                    Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2020,1,14]]},"container-title":["International Journal of Sports Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-1065-2044.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T09:29:04Z","timestamp":1585819744000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-1065-2044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,14]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2020,4,1]]},"published-print":{"date-parts":[[2020,4]]}},"URL":"https:\/\/doi.org\/10.1055\/a-1065-2044","relation":{},"ISSN":["0172-4622","1439-3964"],"issn-type":[{"value":"0172-4622","type":"print"},{"value":"1439-3964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,14]]}}}