Application of deep learning and biological evolution in personalized physical education teaching plan for student physical fitness generation
Abstract
In the realm of physical education in higher education institutions, dance courses have emerged as a vital component due to their holistic nature and technical demands. However, traditional teaching methods often face challenges, including limitations in teaching resources, learning interaction, and motion correction. To address these shortcomings and enhance teaching effectiveness, this study introduces a biometric and motion analysis system tailored for sports dance instruction. Grounded in biomechanical principles and leveraging wearable devices alongside intelligent mobile terminal technology, the system collects kinematic and dynamic data from students’ dance movements. By employing biomechanical models, it quantitatively evaluates movement standardization and provides real-time feedback to students. The research findings demonstrate that this innovative system significantly improves teaching interactivity and student movement accuracy, achieving a 14% increase in teaching efficiency. Furthermore, 93% of students expressed high satisfaction with the system. This study advocates for the integration of mobile intelligent terminals and biometric technology, the optimization of course design, the development of teaching resources guided by biomechanics, and the strengthening of the synergy between practice and theory. By doing so, it aims to establish a more scientific and effective sports dance teaching model.
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