An Optimized Multimodal Feedback System for VR Skill Training Based on ST-GCN and WaveNet

Traditional Virtual Reality (VR) training systems for complex skills requiring motor-auditory coordination are often hampered by feedback latency, multimodal asynchrony, and inaccurate assessment. This paper introduces the Multimodal Synchronous Feedback VR Learning System (MMSFVRLS), built on a parallel processing architecture to address these challenges. Its kinesthetic analysis module employs an optimized Spatio-Temporal Graph Convolutional Network (STGCN), refined through techniques like depthwise separable convolutions and knowledge distillation, for real-time motion analysis. Concurrently, an auditory module integrating WaveNet and Dynamic Time Warping (DTW) provides high-fidelity assessment of vocal performance. Extensive ablation studies validate our architectural design, demonstrating the superiority of the selected components. Our optimized ST-GCN achieves 95.2% accuracy in motion recognition, while the WaveNet-DTW module reaches 92.8% in audio similarity assessment. The integrated system maintains an end-to-end latency of 68.3ms, satisfying the demanding requirements for real-time VR interaction. A user study in Kunqu Opera training confirmed the system’s high practical value and usability (System Usability Scale score: 88.5), proving the effectiveness of our approach for advanced skill acquisition in immersive environments.