{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T19:21:31Z","timestamp":1791832891647,"version":"4.3.4"},"reference-count":41,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009064","name":"Australian Institute of Sport","doi-asserted-by":"publisher","award":["0003223"],"award-info":[{"award-number":["0003223"]}],"id":[{"id":"10.13039\/100009064","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Scandinavian Med Sci Sports"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Two\u2010dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time\u2010consuming manual methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study systematically analyzed the steps required to automate the video analysis workflow by investigating the applicability of a threshold\u2010based event detection algorithm developed for 3D marker trajectories to 2D video data at four sampling rates; the agreement of 2D keypoints estimated by an off\u2010the\u2010shelf pose estimation model compared with gold\u2010standard 3D marker trajectories projected to camera's field of view; and the influence of an offset in event detection on contact time and the sagittal knee joint angle at the key critical events of touch down and foot flat.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Repeated measures limits of agreement were used to compare parameters determined by markerless and marker\u2010based motion capture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results highlighted that a minimum video sampling rate of 100\u2009Hz is required to detect key events, and the limited applicability of 3D marker trajectory\u2010based event detection algorithms when using 2D video. Although detected keypoints showed good agreement with the gold\u2010standard, misidentification of key events\u2014such as touch down by 20\u2009ms resulted in knee compression angle differences of up to 20\u00b0.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      These findings emphasize the need for\n                      <jats:italic>de novo<\/jats:italic>\n                      accurate key event detection algorithms to automate 2D video analysis pipelines.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/sms.14693","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T07:37:11Z","timestamp":1720597031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Automating Video\u2010Based Two\u2010Dimensional Motion Analysis in Sport? 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