Abstract
As society ages, improving the health of the elderly through effective training programs has become a pressing issue. Virtual reality (VR) technology, with its immersive experience, is increasingly being utilized as a vital tool in rehabilitation training for the elderly. To further enhance training outcomes and improve health conditions among the elderly, this work proposes an integrated model that combines the Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Long Short-Term Memory (LSTM) network. The GAN generates realistic, personalized virtual environments, the VAE builds training models closely related to health data, and the LSTM network provides precise motion monitoring and feedback. They collectively improve training effectiveness and assist the elderly in enhancing their health. First, the work optimizes the GAN through alternating training of the generator and discriminator to create personalized virtual environments. Next, the VAE is trained by maximizing the marginal log-likelihood of observed and generated data, and the personalized training model is constructed. Finally, the optimized LSTM network is used to implement a motion monitoring and feedback system. Experimental evaluations reveal that the optimized GAN outperforms the non-optimized version in both image quality scores and diversity indices. The optimized VAE shows improvements in reconstruction error and personalized fitness scores, with a slight reduction in image generation time. Additionally, the training time for the VAE is reduced. After training, the elderly participants exhibit a significant increase in their daily step count and weekly exercise frequency, with p-values less than 0.01, indicating a substantial improvement in their physical activity. Assessments of psychological health show a notable decrease in anxiety and depression scores among the elderly participants.