不显眼的夜间运动监测,以支持养老院的节制护理:算法开发和验证研究
Hannelore Strauven1, Chunzhuo Wang1, Hans Hallez2
1e-Media Research Lab/STADIUS, Department of Electrical Engineering, KU Leuven, Andreas Vesaliusstraat 13, Leuven, 3000, Belgium, +32 16377662.
JMIR nursing
|December 24, 2024
概括
这项研究表明,一个不引人注目的传感器系统可以监测养老院居民的夜间动荡. 这种机器学习方法为改善老年人的节制护理和人工智能支持的医疗保健提供了潜力.
科学领域:
- 老年学是指老年学的学科.
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 尿失禁 (UI) 在老年人中越来越普遍,特别是在养老院 (NHs).
- 需要创新的节制护理解决方案来支持NH居民.
- 隐蔽的传感器系统可以帮助夜间监控与空气排放有关的居民运动和动荡.
研究的目的:
- 探索使用一个不引人注目的传感器系统来监测NH居民的夜间运动.
- 将加速度计传感器集成到护理床和床系统中.
- 分析运动数据,以识别与节制护理相关的特定活动.
主要方法:
- 6名参与者遵循了7个步骤的协议.
- 数据被细分为20%重叠的20秒窗口,并被标记成四个活动类别.
- 一个XGBoost算法分析了1416个特征,并使用离开一个主体的交叉验证 (LOSOCV) 进行验证.
主要成果:
- 训练有素的机器学习模型获得了79.56%的F1总分.
- 具体来说,该模型在检测"激发"方面获得了79.67%的F1得分.
- 这证明了在分类夜间活动时的可靠性能.
结论:
- 不显眼的夜间移动监控显示了增强NH居民护理的有希望的潜力.
- 使用护理床加速计数据的机器学习模型可以改善失禁护理.
- 这项技术推进了对老年人的AI支持的医疗保健.
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