在不同的地形上使用双任务学习和可穿戴传感系统监测落风险
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
这项研究引入了一种新的多传感器系统和双任务学习模型,以准确检测在不同地形上的落风险. 该系统有效地识别了撞击前的跌倒运动,为日益增长的老年人群增强了安全性.
科学领域:
- 老年学和生物医学工程
- 人与计算机的交互
- 机器学习用于医疗保健
背景情况:
- 越来越多的老年人口导致更高的跌倒事故率,需要先进的跌倒风险监测系统.
- 目前用于落检测的深度学习模型经常忽略地形变化,在不均的表面引起虚假警报.
- 准确的跌倒风险评估需要将环境背景,如地形类型,与运动分析相结合.
研究的目的:
- 开发一种新的多传感器系统,用于在不同地形上监测落风险.
- 实施双任务学习 (DTL) 架构,同时进行落下风险和地形分类.
- 评估系统在检测落风险运动和预测落方面的准确性和可靠性.
主要方法:
- 将地形感知传感器与惯性测量单元 (IMU) 集成,以全面获取数据.
- 开发一种经过修改的基于CNN-LSTM的双任务学习 (DTL) 模型,用于分类落风险水平和地形类型.
- 实验验证与年轻受试者在不同的地形和老年人在临床环境中的验证.
主要成果:
- 对于跌倒风险水平 (97.6%) 和地形类型 (95.2%) 的高分类准确度.
- 有效检测撞击前跌落运动,跌落预测准确率为97.7%,平均交付时间为329ms.
- 对于老年人来说,总体监测精度为99.8%,证明了系统的稳定性.
结论:
- 拟议的多传感器系统和DTL模型可靠地监测不同地形的落风险.
- 整合地形感知显著提高了降落风险检测的准确性,并减少了错误警报.
- 这项技术为提高老年人口的安全性和独立性提供了一个有希望的解决方案.
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