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智能座椅移动应用程序,使用TensorFlow lite进行设备内姿势预测.

Saurav Kumar1, Pranav Kashyap Gujja1, Snehith Kongara1

  • 1University of Texas at Arlington Research Institute, Arlington, TX, USA.

Disability and rehabilitation. Assistive technology
|June 21, 2025
PubMed
概括

这项研究开发了一种智能座椅系统,配备了Android应用程序和机器学习,以帮助轮椅使用者预防压力伤害. 该系统提供实时反和姿势监控,以改善对压力再分配指南的遵守.

关键词:
智能座椅枕头是一种智能座椅.移动应用程序移动应用程序姿势预测 姿势预测压力伤害 压力伤害轮椅使用者使用轮椅

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科学领域:

  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术
  • 医疗保健中的机器学习

背景情况:

  • 压力损伤 (PI) 对脊髓损伤 (SCI) 患者来说是一个显著的风险.
  • 对周期性压力再分配 (PR) 的临床指南的低遵守是PI预防的一个主要挑战.
  • 现有的监控系统缺乏实时反,阻碍了一致的公关实践.

研究的目的:

  • 开发和评估一个智能座椅 (SSC) 系统,并集成一个Android应用程序来实时监控和反.
  • 加强轮椅使用者遵守压力再分配协议.
  • 利用机器学习进行准确的姿势预测和用户行为分析.

主要方法:

  • 收集了来自12名健康参与者的9种姿势的坐姿数据.
  • 训练并比较了五种深度学习架构 (MLP,CNN,LSTM,CNN-LSTM,多头注意力) 用于姿势预测.
  • 使用Flutter开发了一个Android应用程序,并通过TensorFlow Lite通过TensorFlow Lite集成了表现最好的LSTM模型 (92%准确度) 来实现设备上部署.

主要成果:

  • 该LSTM模型在姿势预测中实现了92%的准确性,超过了其他深度学习架构.
  • 安卓应用程序成功无线控制SSC,识别座位姿势,可视化压力图,并生成用户统计数据.
  • 该系统提供了实时反和指导,解决了对重量转移协议的低遵守问题.

结论:

  • 开发的SSC系统与人工智能驱动的Android应用程序提供了一个可行的解决方案,以改善对PR协议的遵守.
  • 实时监测,姿势预测和用户反对于有效预防轮椅使用者的压力伤害至关重要.
  • 这一创新代表了PI预防的重大进步,并支持用户遵守临床指南.