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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Yunxia Li,
  • LI Wang,
  • Ziyu Wang,
  • QI Liu,
  • Gang Qin,
  • Jia Zhang

摘要

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.