从实验室到生活:老年人家中的细粒度行为监测

Daniele Riboni, C. Bettini, Gabriele Civitarese, Z. H. Janjua, V. Bulgari
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引用次数: 30

摘要

基于传感器的活动监测系统有望延长体弱老年人的独立生活,包括那些受认知障碍影响的老年人。市场上已经有不同的解决方案,它们使用安装在家中的无线传感器来跟踪老年人的日常生活。这些系统向护理人员提供有关检测到的活动的统计数据;其中一些可能会在识别风险情况时触发实时通知。长期监测细粒度的行为异常可以成为支持神经退行性疾病诊断的重要工具。然而,目前的商业系统只能监控高级活动例程。因此,在之前的工作中,我们设计了一种基于传感器数据识别家中老年人细粒度异常行为的新方法。实验室的实验证明了这种方法的有效性。在本文中,我们介绍了我们在一位诊断为轻度认知障碍的老年人家中实施该系统的经验。在说明了目前的实施情况后,我们讨论了初步结果并概述了研究方向。特别是,初步临床医生的评估表明,该系统支持诊断的潜在效用,以及将系统扩展到监测其他参数(包括神经植物方面和运动行为)将获得的好处。我们还讨论了解决遇到的技术问题的方向,通过更广泛地支持不确定性来改进我们的推理算法,以及通过使老年人成为系统的积极部分来“关闭循环”的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From lab to life: Fine-grained behavior monitoring in the elderly's home
Sensor-based activity monitoring systems promise to prolong independent living of frail elderly people, including those affected by cognitive disorders. Different solutions are already available on the market, which use wireless sensors installed in the home to track the daily living routines of the senior. Those systems provide caregivers with statistics about detected activities; some of them may trigger real-time notifications when they identify a risk situation. Long-term monitoring of finegrained behavioral anomalies can be an important tool to support the diagnosis of neurodegenerative diseases. However, current commercial systems can only monitor high-level activity routines. For this reason, in a previous work we devised a novel method to recognize fine-grained abnormal behaviors of elderly people at home based on sensor data. Experiments in the lab showed the effectiveness of that method. In this paper we present our experience about the implementation of the system in the home of an elderly person with diagnosis of mild cognitive impairment. After illustrating the current implementation, we discuss preliminary results and outline research directions. In particular, a preliminary clinician's assessment indicates the potential utility of this system to support the diagnosis, and the benefits that would be gained by extending the system to monitor additional parameters, including neurovegetative aspects and motor behavior. We also discuss directions for addressing the encountered technological issues, for improving our reasoning algorithms with more extensive support of uncertainty, and for “closing the loop” by making the senior an active part of the system.
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