生产对抗网络和个性化虚拟现实平台在改善老年人脆弱性的影响
1School of Physical Education and Health, East China Jiaotong University, Nanchang, 330001, China.
Scientific reports
|March 11, 2025
概括
本研究介绍了一种针对老年人的综合虚拟现实 (VR) 培训模型,它结合了生成对抗网络 (GAN),变异自编码器 (VAE) 和长短期记忆 (LSTM) 网络. 该模型显著改善了体育活动,并减少了老年参与者的焦虑和抑郁.
科学领域:
- 老年学是一门学科.
- 康复科学 康复科学 康复科学
- 计算机科学 计算机科学
背景情况:
- 人口老龄化需要有效的老年人健康改善策略.
- 虚拟现实 (VR) 为老年人康复培训提供了沉浸式潜力.
- 目前的虚拟现实培训需要加强,以获得个性化和有效的健康结果.
研究的目的:
- 提出和评估一个综合模型,将GAN,VAE和LSTM结合起来,用于老年人VR康复.
- 使用先进的人工智能增强个性化的虚拟环境和培训模型.
- 通过VR,改善老年人的身体活动和心理健康.
主要方法:
- 优化生成对抗网络 (GAN) 用于个性化的虚拟环境生成.
- 训练变量自编码器 (VAE) 构建与健康数据相关的训练模型.
- 实现了长短期记忆 (LSTM) 网络,用于精确的运动监控和反.
- 集成的GAN,VAE和LSTM为一个全面的VR康复系统.
主要成果:
- 与非优化版本相比,优化的GAN显示出更高的图像质量和多样性.
- 优化的VAE显示了减少重建错误和改进个性化的健身得分.
- 老年参与者在每日步数和每周运动频率上呈现出统计学上显著的增加 (p<0.01).
- 在参与者中观察到焦虑和抑郁症得分显著下降.
结论:
- 集成的GAN-VAE-LSTM模型有效地增强了老年人的VR康复培训.
- 该系统导致老年人身体活动水平的显著改善.
- 虚拟现实干预对老年参与者的心理健康产生了积极的影响,减少了焦虑和抑郁.
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