Intelligent optimization of track and field teaching using machine learning and wearable sensors
Yunxia Li, Li Wang, Ziyu Wang, Qi Liu, Qin Gang, Jia Zhang · Scientific Reports · 2025
Traditional track and field education relies heavily on subjective assessment and manual feedback systems, creating critical barriers to personalized instruction in large-scale educational settings. This study presents a novel machine learning framework for optimizing track and field teaching through intelligent analysis and personalization of instructional content. This study developed a multi-layered system architecture integrating wearable IMUs (200 Hz), high-definition cameras (120fps), and force platforms to capture comprehensive biomechanical data from 312 undergraduate participants across three semesters. The system employs a hybrid CNN-BiLSTM architecture with ensemble learning methods for real-time performance analysis. Main Contributions: Our framework introduces (1) an integrated multi-modal sensing system for comprehensive movement analysis, (2) a novel ensemble architecture combining CNN-BiLSTM with gradient-boosted trees for superior classification accuracy, and (3) an adaptive learning optimization algorithm based on reinforcement learning principles. The hybrid CNN-BiLSTM architecture outperformed baseline models in classification tasks for multiple sports with F1-scores ranging from 0.88 to 0.94 and beat the traditional benchmarks by a remarkable 27.3% in time-to-proficiency and 41.2% in injury risk. Validation through ablation studies confirmed that component synergies yielded 17.3% greater performance than individual subsystems. The system shows promise for practical wide-scale implementation in postsecondary education and professional athletic training. This work establishes a foundation for data-driven pedagogical transformation in physical education.