Real-time biomechanical feedback system for swimming turn analysis based on convolutional neural networks and temporal attention mechanism

This paper presents an advanced deep learning framework that integrates convolutional neural networks (CNNs) with temporal attention mechanisms for real-time swimming turn analysis. The proposed architecture features a hybrid spatial-temporal design with multi-scale feature fusion and adaptive normalization, achieving robust performance in challenging underwater environments. The system demonstrates 96.2% accuracy in standard conditions and 91.8% accuracy under low-light scenarios, with a 15% improvement over existing methods. By optimizing computational complexity, the framework achieves 32 frames per second with a 99.99% error recovery rate and a 23% improvement in resource utilization efficiency. Extensive validation shows robust performance across varying water qualities, lighting conditions, and motion scenarios. In addition to its technical robustness, the framework introduces a novel adaptive error handling mechanism, hierarchical state machines, and hybrid deep learning architecture, ensuring stable operation with a mean time between failures (MTBF) of 8760 h and mean time to recovery (MTTR) of 1.2 s. Tested in Olympic-standard facilities, the system reliably delivers precise biomechanical feedback for athletes and coaches. Future research will extend the system to multi-object detection, integrate advanced acoustic sensing for zero-visibility conditions, and explore federated learning for privacy-preserving model updates. This work sets new benchmarks for underwater motion analysis, advancing both athletic training and aquatic research.