[论文解读] Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

Carl Chalmers, Paul Fergus+4 more|arXiv (Cornell University)|Mar 18, 2019

Context-Aware Activity Recognition Systems参考文献 30被引用 11

一句话总结

本文提出了一种非侵入式、基于智能电表的系统，利用负荷分解与机器学习技术检测独居痴呆患者的生活活动（ADL）及日常习惯变化。基于真实世界智能电表数据，采用支持向量机（SVM）与随机决策森林分类器，该系统在电器检测中实现了高精度（AUC=0.9429，灵敏度=0.9634），可实现行为变化的早期检测，从而实现及时的照护干预。

ABSTRACT

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustainable. Technological intervention is being considered, however no solution exists to non-intrusively monitor the independent living needs of patients with dementia. As a result many patients hit crisis point before intervention and support is provided. In parallel, patient care relies on feedback from informal carers about significant behavioural changes. Yet, not all people have a social support network and early intervention in dementia care is often missed. The smart meter rollout has the potential to change this. Using machine learning and signal processing techniques, a home energy supply can be disaggregated to detect which home appliances are turned on and off. This will allow Activities of Daily Living (ADLs) to be assessed, such as eating and drinking, and observed changes in routine to be detected for early intervention. The primary aim is to help reduce deterioration and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation for appliance detection, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine.

研究动机与目标

• 开发一种非侵入式、可扩展的解决方案，用于监测独居痴呆患者，且无需依赖非正式照护者。
• 利用智能电表数据检测日常生活活动（ADL），如进食和饮水。
• 将日常行为的偏离识别为临床恶化早期指标。
• 评估机器学习模型在真实家庭环境中负荷分解精度的表现。
• 通过自动化行为监测，支持独立生活，实现在临床干预上的及时响应。

提出的方法

• 使用信号处理对智能电表数据进行负荷分解，以识别单个电器的使用模式。
• 应用支持向量机（SVM）与随机决策森林分类器，检测电器的开启/关闭状态。
• 利用在三个测试家庭中为期六个月收集的数据进行模型训练与验证。
• 采用受试者工作特征（ROC）分析评估模型性能，使用AUC、灵敏度与特异性指标。
• 将电器级别的事件映射到更高层次的日常生活活动（ADL），如进食、饮水及日常行为变化。
• 通过与默西赛德国家医疗服务体系基金会信托（Mersey Care NHS Foundation Trust）合作，开展临床部署与监测，对两名痴呆患者进行监测。

实验结果

研究问题

• RQ1 智能电表负荷分解能否在真实家庭环境中准确检测单个电器的使用？
• RQ2 机器学习模型能否区分独居痴呆患者正常与异常的行为模式？
• RQ3 电器级别的数据在多大程度上可用于推断更高层次的日常生活活动（ADL）？
• RQ4 SVM与随机决策森林分类器在检测痴呆患者日常习惯偏离方面效果如何？
• RQ5 该系统能否在不依赖非正式照护者的情况下实现临床恶化的早期检测？

主要发现

• 随机决策森林分类器在电器检测中表现优于SVM，AUC达到0.9429，灵敏度为0.9634，特异性也为0.9634。
• SVM模型的AUC为0.86074，灵敏度为0.756，特异性为0.92838，表明性能虽强但略低于随机决策森林。
• 该系统通过电器使用模式成功检测到日常行为习惯的变化，从而实现潜在临床恶化的早期识别。
• 该方法在真实环境中具有可行性，两名痴呆患者在为期六个月的临床试验中得到成功监测。
• 利用智能电表数据进行负荷分解，为独立生活的痴呆患者提供了一种可扩展、非侵入式的连续ADL监测方法。
• 结果表明，通过智能电表实现的自动化、保护隐私的监测，可减少对非正式照护者的依赖，并支持及时的临床干预。