Using Computer Vision and Deep Learning Methods to Capture Skeleton Push Start Performance

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This study aimed to employ computer vision and deep learning methods in order to capture
skeleton push start kinematics. Push start data were captured concurrently by a markerbased motion capture system and a custom markerless system. Very good levels of
agreement were found between systems, particularly for spatial based variables (step
length error 0.001 ± 0.012 m) while errors for temporal variables (ground contact time and
flight time) were within 1.5 frames of the criterion measures. The computer vision based
methods tested in this research provide a viable alternative to marker-based motion
capture systems. Furthermore they can be deployed into challenging, real world
environments to non-invasively capture data where traditional approaches would fail.
Original languageEnglish
Title of host publicationISBS Proceedings Archive
PublisherInternational Society of Biomechanics in Sports (ISBS)
Publication statusPublished - 2 Mar 2020
Event38th International Conference on Biomechanics in Sport - LJMU, Liverpool, UK United Kingdom
Duration: 21 Jul 202025 Jul 2020


Conference38th International Conference on Biomechanics in Sport
Abbreviated titleISBS 2020
Country/TerritoryUK United Kingdom
Internet address


  • Winter sports, computer vision, deep learning, pose estimation.


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