DATA-TRACKING AND PREDICTIVE SIMULATIONS OF SPRINT RUNNING

Authors

Nicos Haralabidis, University of BathFollow
Gil Serrancolí, Universitat Politècnica de CatalunyaFollow
Steffi Colyer, University of BathFollow
Ian Bezodis, Cardiff Metropolitan UniversityFollow
Aki Salo, KIHU Research Institute for Olympic SportsFollow
Dario Cazzola, University of BathFollow

Category

Modelling / Simulation

Document Type

Paper

Abstract

The sprint running literature contains recommendations for how athletes should consider modifying their technique, yet, very few studies have documented their affect on performance. We used a musculoskeletal modelling and simulation approach to initially perform a data-tracking simulation to evaluate the outputs against experimental data. A predictive simulation with limited constraints was then performed to assess the influence of technique modications on performance. The data-tracking simulation tracked the experimental data well, particularly the ground reaction forces (largest RMSE = 0.04 BW). The predictive simulation resulted in the model covering 2.79 m in 0.325 s through an increase in step frequency, and this was a time duration improvement of 6.9% in comparison to the athlete’s own performance. In this preliminary work we have managed to track experimental sprint running data, and provided a promising basis to further explore hypothetical modifications in technique.

Recommended Citation

Haralabidis, Nicos; Serrancolí, Gil; Colyer, Steffi; Bezodis, Ian; Salo, Aki; and Cazzola, Dario (2020) "DATA-TRACKING AND PREDICTIVE SIMULATIONS OF SPRINT RUNNING," ISBS Proceedings Archive: Vol. 38: Iss. 1, Article 200.
Available at: https://commons.nmu.edu/isbs/vol38/iss1/200