Development, evaluation and application of a novel markerless motion analysis system to understand push-start technique in elite skeleton athletes

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This study describes the development, evaluation and application of a computer vision and deep learning system capable of capturing sprinting and skeleton push start step characteristics and mass centre velocities (sled and athlete). Movement data were captured concurrently by a marker-based motion capture system and a custom markerless system. High 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 on average within ± 1.5 frames of the criterion measures. Comparisons of sprinting and pushing revealed decreased mass centre velocities as a result of pushing the sled but step characteristics were comparable to sprinting when aligned as a function of step velocity. There were large asymmetries between the inside and outside leg during pushing (e.g. 0.22 m mean step length asymmetry) which were not present during sprinting (0.01 m step length asymmetry). The observed asymmetries suggested that force production capabilities during ground contact were compromised for the outside leg. 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 are infeasible.
Original languageEnglish
Article numbere0259624
JournalPLoS ONE
Issue number11 November
Publication statusPublished - 15 Nov 2021

Bibliographical note

This research was funded by CAMERA, the RCUK Centre for the Analysis of Motion, Entertainment Research and Applications, EP/M023281/1 and EP/T014865/1 and in collaboration with the British Bobsleigh and Skeleton Association who we thank for their continued time and support with this project. There was no additional external funding received for this study.

Data Availability:
All data from this project relating to athlete performances, e.g. outputs from our proposed system and ground-truth data, are embargoed at the request of our research partners the British Bobsleigh and Skeleton Association (BBSA). The BBSA have requested this data be restricted at the moment, as releasing such data prior to the 2022 Winter Olympic Games could give rival teams the opportunity to analyze the biomechanical techniques of British athletes and as such allow them to gain an unfair advantage. Further restrictions are applied by the University of Bath’s Ethics Committee as data sharing was only permitted between the research team and the BBSA. The University of Bath’s Ethics Committee will consider data requests sent to


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