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

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.

Profiles of stroke regulations discriminate between finishing positions during international open water races.

This study aims to identify stroke regulation profiles and tipping-points in stroke regulation timing during international open water races according to performance level. Twelve elite or world-class swimmers were analysed during 18 international races. Stroke rate and jerk cost were computed cycle-to-cycle using an Inertial Measurement Unit and regulations profiles fitted using polynomials. We performed two-ways mixed-ANOVA to compare stroke kinematics among race segments and performance groups (G1 -fastest- to G3 -slowest-). Swimmers displayed specific regulation profiles (i.e., J-shape with end-spurt, J-shape without end-spurt and reverse L-shape for stroke rate and U-shape, reverse J-shape and reverse L-shape for jerk cost, for respectively G1, G2 and G3) with significant effect of race segment on stroke kinematics for G1 and G2. We highlighted tipping-points in stroke regulations profiles (TP1 and TP2) at respectively 30% and 75% of the race with greater magnitude in G1 than G2. TP1 reflects the end of a stroke economy period (0-30%) and TP2 the end of a progressive increase in stroke kinematics (30-75%) towards end-spurt (75-100%). Open water races follow a high-grading dynamics requiring biomechanical regulations along the race. Targeting stroke rate reserve and management of stroke smoothness should be considered during training of open water swimmers.

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach.

This study presents a deep learning model devoted to the analysis of swimming using a single Inertial Measurement Unit (IMU) attached to the sacrum. Gyroscope and accelerometer data were collected from 35 swimmers with various expertise levels during a protocol including the four swimming techniques. The proposed methodology took high inter- and intra-swimmer variability into account and was set up for the purpose of predicting eight swimming classes (the four swimming techniques, rest, wallpush, underwater, and turns) at four swimming velocities ranging from low to maximal. The overall F1-score of classification reached 0.96 with a temporal precision of 0.02 s. Lap times were directly computed from the classifier thanks to a high temporal precision and validated against a video gold standard. The mean absolute percentage error (MAPE) for this model against the video was 1.15%, 1%, and 4.07%, respectively, for starting lap times, middle lap times, and ending lap times. This model is a first step toward a powerful training assistant able to analyze swimmers with various levels of expertise in the context of in situ training monitoring.