Adaptive ensemble sizing with reinforcement learning for real-time ankle injury detection in wearable sensor systems

Abstract

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Ankle injuries represent a leading cause of long-term impairment for athletes. Wearable inertial sensors have emerged for continuous joint monitoring yet implementing accurate real-time injury detection remains a challenge due to the latency, energy, and computational limitations. Effective solutions must therefore support fast, adaptive, and energy-efficient inference without compromising clinical relevance. We implemented an adaptive ankle injury detection framework using the Ankle Motion Kinematics Dataset (AMKD), which synchronized inertial sensor and video-labeled data from 87 athletes across 12 sports. The system integrates a quantized 1D convolutional neural network (1D-CNN) and a pruned long short-term memory (LSTM) model into a lightweight ensemble. A reinforcement learning (RL) agent dynamically adjusts model parameters based on motion context, informed by a Gaussian process predictor that anticipates future kinematic shifts. The core ensemble model achieved 94.3% classification accuracy on the test set. The full adaptive system, operating under real-time constraints, achieved 87.4% overall detection accuracy and a 12.1% false alarm rate (p < 0.01). It predicted 76.3% of injury events at least 150 ms in advance and maintained a low latency of 17.2 ms 34.8% faster than the best-performing baseline while reducing energy consumption by 35.4% and memory usage by 27.7%. The adaptive controller proactively detected 82.6% of high-risk transitions, and real-world deployment yielded 98.7% uptime across 8-hour sessions, confirming practical viability. These results validate the framework as a viable, low-latency solution for real-time ankle injury detection in sports medicine and rehabilitation settings. Its modularity and efficiency enable seamless integration into existing wearable pipelines while maintaining responsiveness in dynamic conditions.