SiCILIA: A Smart Sensor System for Clothing Insulation Inference

Abstract

In order to maintain productivity and alertness, individuals must be thermally comfortable in the space they occupy (whether it is a cubicle, a room, a car, etc.). However, it is often difficult to non-intrusively assess an occupant's "thermal comfort" and hence most heating, ventilation, and air conditioning (HVAC) engineers adopt fixed temperature settings to "err on the safe side". These set temperatures can be too hot or too cold for individuals wearing different clothing, and as a result lead to feelings of discomfort as well as wastage of energy. To address these challenges, we develop SiCILIA, a platform that extracts physical and personal variables of an occupant's thermal environment to infer the amount of clothing insulation without human intervention. Clothing insulation is one of the most influential factors in determining thermal comfort. The proposed inference algorithm builds upon theories of body heat transfer, and is corroborated by empirical data. Experimental results show that the algorithm is capable of accurately predicting an occupant's thermal insulation with a mean prediction error of approximately 0.2 clo.