在老年人中,社区流动性,生命空间和堕落状态之间的关系
Diego Robles Cruz1,2,3, Andrea Lira Belmar4, Anthony Fleury5
1Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso 2361827, Chile.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
加速度计和GPS数据准确地评估了老年人的跌倒风险. 将传感器与机器学习相结合,可以显著提高对有跌倒史的个体的检测,有助于社区的流动性和自主性.
科学领域:
- 老年学是一门学科.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 社区流动性对于老年人的自主性和社会参与至关重要.
- 量化移动性和评估跌倒风险对于健康的衰老至关重要.
研究的目的:
- 量化老年人的社区流动性.
- 探索GPS和加速度计数据与落风险之间的关系.
- 评估不同传感器数据组合和机器学习模型的有效性,用于降落风险分类.
主要方法:
- 129名老年人 (有/没有跌倒史) 使用GPS和加速度计进行了8小时的监测.
- 分析了来自GPS单独的数据,单独的加速度计和组合传感器的数据.
- 随机森林 (RF),支持矢量机器 (SVM) 和K-近邻 (KNN) 模型被用于分类.
主要成果:
- 单独的加速计数据就产生了很高的准确性 (RF:95%,SVM/KNN:90%).
- 结合GPS和加速度计数据进一步提高了准确性 (RF: 97%,SVM: 95%,KNN: 87%).
- 仅GPS数据的准确性就较低了 (RF:74%,SVM:67%,KNN:62%).
结论:
- 集成的GPS和加速度计数据显著提高了老年人中跌倒风险评估的准确性.
- 基于传感器的方法显示出在社区居住的老年人中识别跌倒风险的巨大潜力.
- 这项技术可以支持维护独立性和社会参与的干预措施.
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