Onshore human swimming motion measurement and dynamic analysis using wearable inertial sensors

Accurately measuring and assessing human swimming performance remains challenging due to difficulties in capturing full-body motion in horizontal postures and evaluating swimming dynamics based on measured data. This study proposes an integrated framework combining wearable inertial measurement units (IMUs), an onshore swim trainer, and a multi-rigid-body dynamic model to measure swimming kinematics and evaluate swimming performance. Seventeen wireless IMUs are used to capture full-body motion data during onshore breaststroke, freestyle, and butterfly strokes, and comparison with optical motion capture data demonstrates that the IMU-based measurement scheme has good validity (Spearman's correlation >0.75), reliability (ICC >0.75), and accuracy (NRMSE <25%) for most body segments. However, lower limb and trunk motions deviate from typical in-water patterns due to restricted downward swing on the onshore trainer. To assess swimming performance with the IMU-measured data, a Newton-Euler dynamic model incorporating fluid forces is developed. Simulations reveal that stroke frequency (SF) has a significant effect on swimming speed and propulsion force across the three strokes. Two case studies further demonstrate the framework's potential for motion optimization: modifying arm movements in freestyle and trunk movements in butterfly can improve swimming performance. Overall, this framework enables reliable and efficient onshore swimming motion measurement, dynamic performance assessment, and individualized technique optimization, which could provide a supplementary tool and preliminary screening method for guiding swimming training and swimming robot development.