What are the main findings? a) A novel IMU-based algorithm combining pelvis vertical velocity (PVV) and the resultant foot acceleration (RFA) signals accurately detected events of initial contact and toe-off during high-dynamic sports movements. b) The algorithm outperforms individual PVV or RFA methods, achieving median offsets below 20 ms compared to force platform data. What is the implication of the main finding? a) The algorithm enables reliable, field-based biomechanical analysis of complex movements, providing a portable and practical alternative to lab-based instrumentation. b) The algorithm can support performance monitoring, injury risk assessment, and rehabilitation in real-world sport settings through IMUs, enhancing ecological validity and accessibility for athletes and clinicians. Abstract: Precise foot contact detection (FCD) is essential for accurate biomechanical analysis in sport performance, injury prevention, and rehabilitation. This study developed and validated an inertial measurement units (IMUs)-based algorithm for FCD during sports movements. Thirty-four healthy athletes (22.8 ± 4.1 years old) performed 90° changes of direction and sprints with deceleration. Data were collected via a force platform (AMTI, 1000 Hz) and a full-body IMU suit (MTw Awinda, Movella, 60 Hz). Two IMU-based algorithms relying on pelvis vertical velocity (PVV) and resultant foot acceleration (RFA), respectively, were tested to detect initial contact (IC) and toe-off (TO). Force platform data served as the gold standard for comparison. Agreement was quantified through median offset and interquartile range (IQR); the influence of task, sex, leg, speed, and acceleration was investigated. The PVV algorithm showed higher offset than RFA for IC detection (16.7 ms vs. 10.2 ms) with comparable IQR and a substantially higher offset for TO (102.8 ms vs. 20.4 ms). Minimal influence of co-factors emerged (variance < 10%). Results were sensibly improved by combining PVV and RFA, for both IC (5.6 [70.4] ms) and TO (20.4 [78.7] ms). This algorithm offers a robust, portable alternative to force platforms, enabling accurate footstep detection and analysis of complex, sports movements in real-world environments, enhancing the ecological validity of sport assessments.
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