Affective intelligence in built environments

Abstract

The contribution of the proposed dissertation is the application of affective intelligence in human-developed spaces where people live, work, and recreate daily, also known as built environments. Built environments have been known to influence and impact individual affective responses. The implications of built environments on human well-being and mental health necessitate the need to develop new metrics to measure and detect how humans respond subjectively in built environments. Detection of arousal in built environments given biometric data and environmental characteristics via a machine learning-centric approach provides a novel and new capability to measure human responses to built environments. Work was also conducted on experimental design methodologies for multiple sensor fusion and detection of affect in built environments. These contributions include exploring new methodologies in applying supervised machine learning algorithms, such as logistic regression, random forests, and artificial neural networks, in the detection of arousal in built environments. Results have shown a machine learning approach can not only be used to detect arousal in built environments but also for the construction of novel explanatory models of the data.