An IMU-validated artificial intelligence framework for kinematic fatigue detection and performance assessment in table tennis training
In table tennis, repeated high-speed strokes can produce measurable changes in racket acceleration, movement regularity, and technical stability, yet field-based fatigue monitoring remains difficult during normal training. This study evaluates an inertial measurement unit (IMU)-based artificial intelligence framework for kinematic fatigue-compatible drift and performance assessment using the public TTSwing corpus, while specifying heart rate variability (HRV) only as a future synchronized physiological extension. The dataset contains 97,350 racket-grip swing records from 93 elite Taiwanese players collected with embedded 9-axis inertial sensors. Full-power stroke sessions were analyzed through within-session trend modeling, spectral-entropy descriptors, composite fatigue proxies, effect-size estimation, and player-independent machine-learning validation. Peak acceleration, root-mean-square acceleration, acceleration spectral entropy, and angular-velocity spectral entropy showed statistically significant but small-to-modest declines across repeated swings. These patterns are interpreted as candidate proxies for kinematic fatigue rather than direct evidence of physiological fatigue, as HRV, electromyography, perceived exertion, and coach-rated fatigue labels are unavailable. The Mechanical Fatigue Index showed strong reliability, whereas the Neuromotor Complexity Index remained exploratory. Classification results were modest and not ready for deployment in autonomous coaching decisions. The study offers a leakage-controlled IMU, evidence-based, clearer practical boundaries, and a cautious pathway for future HRV-synchronized multimodal validation in racket-sport training under controlled, prospective fatigue protocols.
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