Research on the Application of Deep Learning and VR Technology in Youth Physical Education Teaching

Physical education among young people contributes greatly to establishing a lifelong lifestyle of health, discipline, and mental development. Nevertheless, existing learning systems are marred with various shortcomings, such as no interactivity, generalized teaching practices, and no immediate personalized feedback. Such conventional strategies do not address the varied learning requirements of students and consequently result in decreased motivation and ineffective skill acquisition. To address these challenges, this research introduces a novel intelligent teaching framework that combines Deep Learning with Virtual Reality technologies for delivering adaptive, engaging, and feedback-rich physical education experiences. The system under consideration consists, Sensor-based motion data acquisition, VR-based immersive sports simulation, Real-time performance evaluation and feedback module, and Adaptive recommendation system for skill improvement. Deep learning allows the posture and actions to be accurately identified in real-world physical training environments. Collectively, this integration substantially increases learning interest, participation rate, and skill precision. To test the performance of the proposed framework, extensive experiments were performed with typical algorithms like Convolutional Neural Networks, Long Short-Term Memory Evaluation metrics like accuracy 96.50%, precision 96.50%, recall 96.50%, and an F1-score of 96.50% were employed to compare the systems. This smart system is a promising advancement in the move towards the modernization of youth physical education and is likely to continue to advance with future integration of biometric sensors and reinforcement learning for real-time adaptation of skills and overall growth.