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基于GCN-LSTM多任务学习的老年人落检测,使用与多阵列灵活触觉传感器集成的护理辅助器件.

Tong Li1, Yuhang Yan1, Minghui Yin2,3

  • 1School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China.

Biosensors
|September 27, 2023
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概括

这项研究引入了一种新型的跌倒检测系统,使用集成在护理辅助器件中的触觉传感器. 该方法达到96.36%的准确性,为老年人预防跌倒提供了具有成本效益和强大的解决方案.

关键词:
老年人跌倒检测检测器多阵列的灵活触觉传感器多任务学习是多任务学习.护理辅助工具 护理辅助工具触觉序列的触觉序列是指一个触觉序列.

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科学领域:

  • 生物医学工程 生物医学工程
  • 老年学是一门学科.
  • 传感器技术 传感器技术

背景情况:

  • 老年人跌倒对健康构成重大风险,需要有效的检测方法.
  • 目前的摔倒检测系统通常依赖于昂贵和复杂的视觉或多传感器设备.
  • 由于成本和设计复杂性,现有方法的适用性有限.

研究的目的:

  • 为老年人开发一种具有成本效益和广泛适用的跌倒检测方法.
  • 提出一种使用护理辅助工具,集成多阵列灵活触觉传感器的摔倒检测系统.
  • 为了利用足部力分析和触觉数据进行准确的摔倒检测.

主要方法:

  • 设计和实施多阵列电容触摸传感器.
  • 根据脚部力分析,传感器在脚上的分布.
  • 使用图形卷积神经网络 (GCN) 和长期短期记忆 (LSTM) 网络 (GCN-LSTM) 开发掉落检测模型.
  • 使用GCN和LSTM模块从触觉序列中提取空间和时间特征.

主要成果:

  • 对于预测的触觉数据,实现了0.0716的平均平方误差 (MSE).
  • 展示了96.36%的跌落检测准确度.
  • 成功执行未来的跌倒检测到5个时间步骤 (0.2秒间隔) 的高可靠性.
  • 在不同的地形类型和形态学中展示了强大的概括能力.

结论:

  • 拟议的GCN-LSTM模型有效地使用护理辅助器的触觉数据检测掉落.
  • 这种基于触觉传感器的方法为现有的落检测系统提供了一个有希望,准确和强大的替代方案.
  • 该方法显示出在老年护理中广泛采用的潜力,因为它的成本效益和设计简单.