Dynamic monitoring and adaptive algorithm optimization of fatigue recovery in track and field athletes based on inertial measurement unit sensor data.

Introduction: Sprint recovery monitoring requires individualized assessment because movement mechanics return toward baseline at different rates following repeated high-intensity loading. This study developed Sprint Recovery Adaptation (SRA), an adaptive framework for individualized sprint-kinematic recovery monitoring in 100 m and 200 m sprinters. Methods: A repeated-measures field algorithm-validation design included 124 Chinese collegiate sprinters who completed baseline testing, repeated 60 m fatigue-induction sprints, and recovery assessments immediately post-fatigue and at 5, 10, 15, and 20 min. SRA integrated inertial measurement unit (IMU)-derived acceleration consistency, angular-velocity control, trunk-pelvis stability, limb symmetry, individualized baseline normalization, and temporal updating. Recovery categories were defined according to predefined testing intervals and individualized mechanical deviation criteria. Because no independent physiological reference measure was included, interpretation was restricted to sprint-kinematic recovery. An independent endurance-running IMU dataset was additionally used for cross-task feature-generalization testing. Results: The Recovery Index increased from 54.20 ± 8.30 immediately post-fatigue to 86.80 ± 6.40 at 20 min. Athlete-level held-out validation yielded an accuracy of 0.864, an F1-score of 0.861, and an area under the curve of 0.917, with SRA outperforming the strongest fixed comparison model. Ablation analysis identified individualized baseline normalization and temporal updating as major contributors to classification performance. Cross-task testing using the independent endurance-running IMU dataset supported the generalizability of the feature-processing pipeline. Discussion: SRA provides a feasible approach for short-term, individualized monitoring of post-fatigue sprint-mechanics recovery. Its adaptive use of athlete-specific baselines and temporal updating appears to improve recovery-state classification compared with fixed models. However, the framework should be interpreted as a measure of kinematic recovery rather than physiological recovery, and further validation against independent physiological and performance-based reference measures is warranted.