Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Abstract

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper, showing the variability of noise-related distractions, thermal comfort, and associated context. The results show that participants experienced at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and the collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants’ needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.

Human Behavior Adaptability in Responsive Buildings: An Exploratory Study in Workplace Settings

“…Advancements in information and communication technologies (ICTs) have facilitated a systematic study of human behavior in buildings to support a variety of applications, including occupant presence and behavior analyses [39][40][41][42], space-use matching [14], and building systems automation [43,44]. The integration of sensor data with digital building models has given rise to 'digital twin' systems, providing a real-time overview of the current state of a facility for building management [45,46] and sustainability assessments [47].…”