A theoretical framework for classifying occupant-centric data streams on indoor climate using a physiological and cognitive process hierarchy

Abstract

New and pervasive information and communication technologies have made it possible to capture a large range of continuous data from, or close to, each individual building occupant. These occupant-centric data streams may include subjective votes, evaluations, complaints, control actions, physiological measurements such as heart rate or pupil size, physical measurements of skin temperature or local draft and air temperature measurements, and much more. Currently, considerable resources are put into studies that focus on the development and potential uses of such systems, while the origin and nature of the collected information which is embedded in the data is poorly investigated. In this paper, we propose a taxonomy for the classification of occupant-centric data streams, developed through the application of established theories and categories in environmental and market psychology. The proposed framework organises five data source categories and links them to four levels of physiological and cognitive processes, making an explicit connection between data and embedded information attributes. The framework, originally developed to classify continuous occupant centric data in the domain of indoor climate, can also bring insights that might help explain known gaps and challenges in different models and theories that aim at predicting individual satisfaction with indoor climate conditions.