Department of Computer Science, the University of Texas Rio Grande Valley, Edinburg, TX 78539, USA.
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99163, USA.
Sensors (Basel). 2020 Sep 12;20(18):5207. doi: 10.3390/s20185207.
Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm-Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)-to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual's behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident's cognitive health diagnosis, with an accuracy of 0.84.
数字行为标记可以使用环境传感器收集的时间序列数据，在日常环境中不断创建。这项工作的目的是从这些时间序列传感器数据中进行个体和群体行为分析。在本文中，我们介绍了一种新的算法——居民相对熵-逆强化学习（RRE-IRL），使用逆强化学习对单个智能家居居民或一组居民进行分析。通过采用这种方法，我们学习了个人的行为常规偏好。然后，我们分析了个人和按健康诊断分组的 8 位智能家居居民的日常常规。我们观察到，行为常规偏好随时间而变化。具体来说，与认知健康的居民相比，经历认知能力下降的居民在数据收集开始时和结束时（几个月后）观察到的行为相同的概率较低。当比较来自两个诊断组的居民群体的聚合行为时，行为差异更大。此外，行为偏好被随机森林分类器用于预测居民的认知健康诊断，准确率为 0.84。
