Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations

Abstract

ABSTRACT As one of the representative parameters for human energy metabolism, the metabolic rate has been considered as the significant factor for occupants’ thermal comfort analyses. Despite the importance of metabolic rate as a predictor of thermal comfort modelling, prior works rely on uncertain metabolic rate estimation without considering actual activity variations while occupying a building. This study aims at identifying the effect of metabolic rate on the thermal comfort models by proposing a robust data-driven personalized model in consideration of human activity variations. To investigate heterogeneous thermal state of occupants, wearable sensors and machine learning algorithms were used to continuously monitor and analyse individual physiological signals, activity-based metabolic rates and environmental indices. Field experiments were conducted with 10 subjects in a campus building in the US, and the results showed that predictive models considering metabolic rate yield advanced performance of up to 8.5%, implying that activity-based metabolic rates provide better understanding of personal thermal comfort. This paper quantitatively validates the effectiveness of reflecting metabolic rate based on human activity variations into personal thermal comfort modelling, which provides an insight into how to better model personal thermal comfort of occupants in real-life settings.