Intelligent optimization of track and field teaching using machine learning and wearable sensors.

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.