Construction of a sports bio mechanical injury prediction and AI warning system based on wearable sensors

BACKGROUND: Real-time warning and prevention of sports injuries are core challenges in the fields of sports medicine and health management. Traditional methods rely on single sensor data and static threshold rules, which have problems such as single monitoring dimension, high false alarm rate, and insufficient real-time performance, making it difficult to meet the precise prevention and control needs in complex motion scenes. METHODS: In response to the above issues, this study proposes a sports biomechanical injury prediction and real-time warning system based on wearable sensors. Through multimodal data fusion, lightweight model design, and a dynamic adaptive mechanism, the system enables accurate identification and real-time intervention for high-risk movements. The system integrates nine-axis inertial sensors, flexible strain sensors, and plantar pressure sensing arrays to synchronously collect multidimensional biomechanical parameters (e.g., joint kinematics, local tissue deformation, and ground reaction forces). By combining an improved hybrid neural network architecture, extracting spatiotemporal features, and dynamically weighting key action segments, the system significantly enhances the sensitivity and specificity of injury prediction. Through the edge computing optimization strategy, the model is quantized and compressed to 8-bit integer, and the end-to-end delay is controlled at the millisecond level. Power consumption is reduced sufficiently to support embedded deployment. RESULTS: Experimental validation showed that the dynamic threshold adjustment mechanism reduced both the false alarm rate and the missed alarm rate on the same evaluation set. In basketball stop-jump tasks, the system identified elevated knee valgus risk before the left-knee peak angle reached 16.2° at 400 ms. In long-distance running surveillance, progressive gait asymmetry was detected by Week 3 in the high-risk group, and peak load decreased by 18% after intervention. Together, these findings support the value of synchronized multimodal sensing, serial deep learning, and adaptive warning logic for proactive sports injury prevention. CONCLUSIONS: Future work will focus on multi center data sharing, wearable exoskeleton linkage intervention, and augmented reality visualization feedback under the federated learning framework, further expanding the application scenarios and clinical value of the system.