Yu Zhendong, Dang Jianan
School of Physical Education and Health, East China Jiaotong University, Nanchang, 330001, China.
College of Education, University of the Visayas, Cebu, 6000, Philippines.
Sci Rep. 2025 Mar 10;15(1):8220. doi: 10.1038/s41598-025-93553-w.
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.
随着社会老龄化，通过有效的培训项目改善老年人健康已成为一个紧迫问题。虚拟现实（VR）技术凭借其沉浸式体验，越来越多地被用作老年人康复训练的重要工具。为了进一步提高训练效果并改善老年人的健康状况，这项工作提出了一种整合模型，该模型结合了生成对抗网络（GAN）、变分自编码器（VAE）和长短期记忆（LSTM）网络。GAN生成逼真的个性化虚拟环境，VAE构建与健康数据密切相关的训练模型，LSTM网络提供精确的运动监测和反馈。它们共同提高训练效果，帮助老年人增强健康。首先，通过生成器和判别器的交替训练对GAN进行优化，以创建个性化虚拟环境。接下来，通过最大化观测数据和生成数据的边际对数似然对VAE进行训练，并构建个性化训练模型。最后，使用优化后的LSTM网络实现运动监测和反馈系统。实验评估表明，优化后的GAN在图像质量得分和多样性指数方面均优于未优化版本。优化后的VAE在重建误差和个性化适应度得分方面有所改善，图像生成时间略有减少。此外，VAE的训练时间也减少了。训练后，老年参与者的每日步数和每周锻炼频率显著增加，p值小于0.01，表明他们的身体活动有了实质性改善。心理健康评估显示，老年参与者的焦虑和抑郁得分显著降低。
