Measuring Success, One Sensor at a Time: A Sensing Infrastructure for Longitudinal Workspace Behavior Monitoring

The development of built environments has expanded its focus beyond sustainability to health due to the potential to become a transformative health tool. Studies have shown that exposure to poor Indoor Environmental Quality (IEQ) can affect health and productivity, manifesting itself as sick building syndrome (SBS) and economic losses. Although previous studies have used wearables, mobile sensing, environmental sensing, and self-reports, none have utilized all four in a naturalistic environment. This study introduces a naturalistic multi-modal occupant state and behavior monitoring framework that (1) collects occupant and environment data through smart wearable devices, smartphones, environmental sensors, and self-reported surveys; (2) processes data to gain insight into participants. To assess the validity and ease of use of this framework, a pilot study was conducted with 13 participants, some in a smart office building space and others in their homes. This preliminary study confirmed the feasibility of employing surveys, smart wearable devices, environmental sensors, and passive smartphone sensing in a multi-modal approach to monitor occupant states and behaviors within smart office settings. The results revealed a positive linear relationship (r = 0.2497, 95% CI of [0.1502, 0.3493]) between subjective sleep ratings and total sleep time (TST). The reported sleep times were cross-validated with the identified sleep cycles, confirming the reliability of smart wearables in assessing sleep. Furthermore, in both the office and home participant data, noise levels exhibited a high incidence of coinciding change points with physical activity, suggesting a relationship between shifts in noise levels and changes in the physical activity of the occupant and vice versa. These findings further underscore the complex interaction between environmental factors and human behavior in the wild that can be closely examined in the proposed framework.