Inertial Measurement Units (IMUs) offer promising alternatives to traditional motion capture systems, especially in real-world sports scenarios. Accurate foot contact detection (FCD) is crucial for biomechanical analysis, and since on-the-field force plates are unsuitable, IMU-based FCD algorithms have been extensively investigated. However, sports activities leading to musculoskeletal injuries are multidirectional and high-dynamics in nature and FCD algorithms, which have mostly been studied in gait analysis, might sensibly worsen performance. This systematic review (PROSPERO, ID: CRD420251010584) aimed to evaluate IMU-based FCD algorithms applied to high-dynamics sports tasks, identifying strengths, limitations, and areas for improvement. A multi-database search was conducted until May 2025. Studies were included if they applied IMU-based FCD algorithms in high-dynamic movements. In total, 37 studies evaluating 71 FCD algorithms were included. Most papers focused on running, with only 3 on cut manoeuvres. Almost all studies involved healthy individuals only, and foot linear acceleration was the most inspected FCD metric. FCD algorithms demonstrated high accuracy, though speed variation impacted performance in 23/37 studies. This review highlights the lack of validated IMU-based FCD algorithms for high-dynamic sports movements and emphasizes the need for improved methods to advance sports biomechanics testing in injury prevention.

Anterior cruciate ligament (ACL) injury risk is elevated in female youth football, yet knee joint loading has mainly been studied under controlled laboratory conditions. This limits understanding of how injury risk emerges during realistic match situations. This study provided a field-based kinetic characterization of football-specific movements by estimating knee abduction moments (KAMs) using wearable sensors and machine learning. Fifty-two highly talented female youth players performed agility tasks during training, including structured exercises (F-EX) and game-based play (F-GAME). Full-body kinematics were collected with inertial measurement units, and a validated support vector machine model, trained on synchronized motion capture and force plate data, classified trials as high or low KAM. Across 662 change-in-direction trials, 9–12% were classified as high KAM in both conditions, indicating that potentially high-risk loading regularly occurs during routine actions. High KAM trials showed reduced knee and pelvis flexion, increased hip flexion, and greater pelvis rotation toward the cutting direction, reflecting upright, stiff movement strategies. Performance analyses revealed smaller cut angles in exercises and greater approach acceleration in game play, without differences in peak velocity. These findings demonstrate the feasibility of field-based kinetic screening and support a complex-systems perspective on ACL injury risk.