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

Abstract

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.

Fig 1. Schematic of the experimental set-up at the push-track (top) and on the indoor track (bottom).

The marker-based and markerless cameras are denoted in black and green, respectively. The capture volume at the push-track was ~10 m long and was centred ~10-m from the block (red dashed area). On the indoor track, a ~10 m volume was captured across multiple trials with three different starting positions. This allowed a 0–30 m section to be reconstructed (red dashed area).

Fig 2. Step characteristics during sprinting (red) and pushing (blue) shown as a function of step velocity.

Fig 3. Paired step characteristic differences for inside and outside leg during pushing (left column) and the corresponding leg during sprinting (right column).

The paired Cohen’s d is plotted on floating axes on the right as a bootstrap sampling distribution. The Cohen’s d effect size is depicted as a dot; the 95% confidence interval is indicated by the ends of the vertical error bar. Top row—SL, centre row—SF, bottom row—SV.

Fig 4. Paired step characteristic differences for inside and outside leg during pushing (left column) and the corresponding leg during sprinting (right column).

The paired Cohen’s d is plotted on floating axes on the right as a bootstrap sampling distribution. The Cohen’s d effect size is depicted as a dot; the 95% confidence interval is indicated by the ends of the vertical error bar. Top row—GCT, bottom row—FT.