Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Sensors

Beiyu Lin¹  Diane J. Cook² 

[1] 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;

关键词: smart homes;    ambient sensors;    activity recognition;    behavior analysis;    inverse reinforcement learning;   

DOI  :  10.3390/s20185207

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown