Real-time biomechanical feedback system for swimming turn analysis based on convolutional neural networks and temporal attention mechanism
Year: 2025
Type: article
Abstract: 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.
DOI: 10.62617/mcb1695
Authors: Can Huang, Qi Meng
Institution: Chengdu Sport University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408418652