Profiling biomechanical abilities during sprint front-crawl swimming using IMU and functional clustering of variabilities

ABSTRACT

This study aims to profile biomechanical abilities during sprint front crawl by identifying technical stroke characteristics, in light of performance level. Ninety-one recreational to world-class swimmers equipped with a sacrum-worn IMU performed 25 m all-out. Intra and inter-cyclic 3D kinematical variabilities were clustered using a functional double partition model. Clusters were analysed according to (1) swimming technique using continuous visualisation and discrete features (standard deviation and jerk cost) and (2) performance regarding speed and competition calibre using respectively one-way ANOVA and Chi-squared test as well as Gamma statistics. Swimmers displayed specific technical profiles of intra-cyclic (smoothy and jerky) and inter-cyclic stroke regulation (low, moderate and high repeatability) significantly discriminated by speed (p < 0.001, η² = 0.62) and performance calibre (p < 0.001, V = 0.53). We showed that combining high levels of both kinds of variability (jerky + low repeatability) are associated with highest speed (1.86 ± 0.12 m/s) and competition calibre (ℽ = 0.75, p < 0.001). It highlights the crucial importance of variabilities combination. Technical skills might be driven by a specific alignment of stroke pattern and its associated dispersion according to the task constraints. This data-driven approach can assist eyes-based technical evaluation. Targeting the development of an explosive swimming style with a high level of body stability should be considered during training of sprinters.

KEYWORDS:

• Swimming
• inertial measurement unit
• technical skills
• movement variability
• functional data analysis

Acknowledgments

We are grateful to the athletes and the staff for their kind collaboration.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the French Agence Nationale de la Recherche within the NePTUNE project (ANR-19-STPH-004) and conducted within the framework of the PIA EUR DIGISPORT project (ANR-18-EURE-0022). Antoine Bouvet was supported by a Ph.D. scholarship from the Ecole Normale Supérieure de Rennes.