Using continuous sensor data to formalize a model of in-home activity patterns

Abstract

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for selected activity classes in the smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same smart home data to examine the relationship between the formal model and resident health status. Our study reveals that human indoor activities can be described by non-Poisson processes and that the corresponding distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the combination of activities in certain subgroups can be described by multivariate Pareto distributions. These findings will help researchers understand indoor activity routine patterns and develop more sophisticated models of predicting routine behaviors and their timings. Eventually, the findings may also be used to automate diagnoses and design customized behavioral interventions by providing activity-anticipatory services that will benefit both caregivers and patients.

Keywords

Human dynamics population modeling Pareto distribution pervasive environment activity recognition

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