Because of the worldwide aging population, more and more elders suffer from dementia problem. Nowadays, it is an inconvenient and time-consuming process for medical doctors to diagnose elders who live independently with possible dementia because the process imposes a large quantity of diagnostic questions from a checklist that needs to be answered by elders themselves or their caregivers either directly or after a long-term observation. In order to help doctors to make this diagnostic process easier, this article proposes a supporting system that can quickly estimate the likelihood for an elder of having dementia based on 2 to 4 hours monitoring of a behavioral test done by the elder. During the test, the elder only needs to perform certain activities selected from the so-called instrumental activities of daily living (IADL) in a smart home environment, and their movement trajectories will be extracted from motion sensors deployed in the smart home environment and be analyzed to find a potential correlation with the indoor wandering patterns. A machine learning algorithm is selected to carry out the classification task, namely, into dementia and nondementia groups, based on our proposed features of the aforementioned wandering patterns. Two data sets are employed for performance evaluation, where the first one is 232 elders including seven dementia, whereas the second one is collected by ourselves from a senior center, which is 30 elders including nine dementia. It turns out that the average precision and recall for the first data set are both up to 98.3% with area under the ROC curve (AUC-ROC) being 0.846, and those for the second data set are 89.9% and 90.0% with AUC-ROC being 0.921. Note to Practitioners —We proposed a supporting system which can classify the elders as either dementia or nondementia with high accuracy. The trajectories of the elders will be extracted from motion sensors that deployed in the smart home environment. The indoor wandering patterns according to repetitive movements are analyzed and classified using the machine learning technique. The proposed system used ambient sensors instead of wearable sensors or cameras to let the elders feel more comfortable when they are being monitored. In addition, the proposed system only required a short period of time to screen the elders and easier for medical doctors to diagnose the elders without wasting time for asking the large quantity of diagnostic questions from a checklist that needs to be answered by the elders themselves or their caregivers.

中文翻译：

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用于大规模痴呆筛查的快速低成本重复运动模式指示器

由于全球人口老龄化，越来越多的老年人患有痴呆症问题。如今，对于医生而言，诊断可能患有痴呆症的独立生活的老年人是一种不便且耗时的过程，因为该过程会从清单中强加大量诊断问题，需要老年人本人或其照料者直接或直接回答。经过长期观察。为了帮助医生简化此诊断过程，本文提出了一种支持系统，该系统可以根据对老年人进行的行为测试的2到4个小时的监测，快速估计老年人患有痴呆症的可能性。在测试期间 老年人只需要在智能家居环境中执行从所谓的日常生活工具活动（IADL）中选择的某些活动，他们的运动轨迹就会从部署在智能家居环境中的运动传感器中提取出来，并进行分析以找到与室内漫游模式的潜在相关性。基于我们提出的上述漂移模式的特征，选择一种机器学习算法来执行分类任务，即分为痴呆和非痴呆组。使用两个数据集进行绩效评估，第一个数据集是232名长者，包括七个痴呆症，而第二个数据集是我们自己从一个高级中心收集的，其中包括三十个长者，其中包括九个痴呆症。执业者注意 -我们提出了一种支持系统，可以将老年人准确分类为痴呆症或非痴呆症。长者的轨迹将从部署在智能家居环境中的运动传感器中提取。使用机器学习技术对根据重复运动的室内游荡模式进行分析和分类。拟议的系统使用环境传感器代替可穿戴式传感器或照相机，以使年长者在受到监视时感觉更舒适。另外，所提出的系统仅需要很短的时间来筛查长者，并且使医生更容易诊断长者，而不会浪费时间从清单中询问大量诊断问题，而这些清单需要长者自己或长者回答。他们的照顾者。