Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.
Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.
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DOI：
10.1109/jbhi.2015.2445754
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发表时间：
2016-07
影响因子：
7.7
通讯作者：
Schmitter-Edgecombe M
中科院分区：
文献类型：
--
作者：
Dawadi PN;Cook DJ;Schmitter-Edgecombe M
智能家居技术通过自动化健康监测和健康评估为临床医生提供了潜在的好处。在本文中，我们通过监测家庭中的日常行为和预测居民的标准临床评估分数来研究基于智能家居的分析的实际好处。为了实现这一目标，我们提出了一个临床评估使用活动行为（CAAB）的方法来模拟智能家居居民的日常行为，并预测相应的标准临床评估分数。CAAB使用描述居民日常活动表现特征的统计特征来训练机器学习算法，以预测临床评估分数。我们评估CAAB的性能，利用智能家居传感器数据收集的18个智能家居在两年多的预测和分类为基础的实验。在基于预测的实验中，我们获得了CAAB预测和临床医生提供的认知评估评分之间的统计学显著相关性（r = 0.72），以及CAAB预测和临床医生提供的移动性评分之间的统计学显著相关性（r = 0.45）。同样，对于基于分类的实验，我们发现CAAB在分类认知评估分数时的分类准确率为72%，在分类移动性分数时的分类准确率为76%。这些预测和分类结果表明，使用智能家居传感器数据和基于学习的数据分析来预测标准临床评分是可行的。
Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis.