Abstract
The development of an immersive virtual simulation training platform designed to enhance physical education by integrating biomechanics for precise posture training. Through the use of biomechanical analysis and virtual reality (VR), the platform offers real-time feedback and assessment, helping students to comprehend and correct their posture while engaging in physical activities. The research introduced an Improved Aquila Optimized Efficient Deep Convolution Neural Network (IAO-EDCNN) model to estimate joint kinematics and strength metrics, which are crucial for posture accuracy and injury prevention. To collect data from VR-compatible sensors to gather motion data continuously while users perform various physical exercises. The data was preprocessed using a Gaussian Filter to smooth data and reduce high-frequency noise in the data. Frequency-domain characteristics were extracted using the Fast Fourier Transform (FFT) as dominant frequency components of motion. IAO model ensures that the joint angles and positions during exercises are optimal and EDCNN can be employed to analyze motion capture data, assess joint kinematics, and predict strength metrics. The results indicate that upper-body kinematics can be accurately estimated with less error for joint angles, allowing for reliable real-time feedback during sessions. In a comparative analysis, the suggested method is assessed with various evaluation measures, such as F1-score (95.60%), recall (95.35%), precision (96.10%), and accuracy (95.85%). The result demonstrated the IAO-EDCNN method to estimate joint kinematics and strength metrics, which are crucial for posture accuracy and injury prevention. This innovative approach demonstrates that VR technology, paired with biomechanics can serve as an effective tool for posture training in physical education. By providing accessible, evidence-based metrics, this platform aims to enhance the quality of physical education through immersive and engaging training experiences.
Published Version
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