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Abstract:In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and evaluate the approach using two smart homes. The findings may help us understand the types of behavior that measurably impact indoor air quality. This information could help us plan for the future by developing an automated building system that would be used as part of a smart city.

In order to meet the health needs of the coming "age wave", technology needs to be designed that supports remote health monitoring and assessment. In this study we design CIL, a clinician-in-the-loop visual interface, that provides clinicians with patient behavior patterns, derived from smart home data. A total of 60 experienced nurses participated in an iterative design of an interactive graphical interface for remote behavior monitoring. Results of the study indicate that usability of the system improves over multiple iterations of participatory design. In addition, the resulting interface is useful for identifying behavior patterns that are indicative of chronic health conditions and unexpected health events. This technology offers the potential to support self-management and chronic conditions, even for individuals living in remote locations.

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Formal modeling and analysis of human behavior can properly advance disciplines ranging from 3 psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-4 valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior 5 based on data from automatically-sensed and recognized activities. A mechanistic description of behavior 6 patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and 7 compare these norms between subgroups. We identify and model the patterns of human behaviors based on 8 inter-arrival times, the time interval between two successive activities, for selected activity classes in the 9 smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities 10 in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same 11 smart home data to examine the relationship between the formal model and resident health status. Our study 12 reveals that human indoor activities can be described by non-Poisson processes and that the corresponding 13 distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the 14 combination of activities in certain subgroups can be described by multivariate Pareto distributions. These 15 findings will help researchers understand indoor activity routine patterns and develop more sophisticated 16 models of predicting routine behaviors and their timings. Eventually, the findings may also be used to 17 automate diagnoses and design customized behavioral interventions by providing activity-anticipatory 18 services that will benefit both caregivers and patients.