Design and Research of a Stress-Relief and Mindfulness Virtual Reality Training Environment Based on Generative Adversarial Networks

Virtual reality (VR) systems have emerged as a promising tool for stress relief and mindfulness training, offering immersive and interactive environments tailored to users’ psychological needs. However, traditional VR systems often rely on static, pre-designed content, limiting their adaptability and effectiveness. This study introduces an innovative VR training system powered by Generative Adversarial Networks (GANs), enabling dynamic and personalized content generation in real-time. The system employs a modular architecture comprising hardware integration, GAN-based content generation, real-time user interaction, and feedback-driven adaptation. Key advancements include optimizing the GAN model for computational efficiency and content realism, ensuring seamless real-time performance while maintaining high-quality visual outputs. Experiments conducted with 50 participants demonstrated significant improvements in content quality (PSNR: 38.5 dB, SSIM: 0.92), real-time rendering performance (60 FPS), and user satisfaction (Likert scale: 4.6). Comparative analysis highlighted the superiority of the optimized GAN-based system over traditional VR systems and non-optimized GAN models. These findings underscore the practical value of the proposed system in delivering scalable, adaptive, and immersive solutions for stress relief and mindfulness training, making it a robust tool for addressing mental health challenges.