SiCILIA: a smart sensor system for clothing insulation inference using heat exchange

Abstract

We present SiCILIA, a hardware platform that extracts physical and personal variables of an individual's thermal environment to infer the amount of clothing insulation and thermal sensation without human intervention. The proposed inference algorithms build upon theories of body heat transfer, and are corroborated by empirical data. Experimental results show the algorithm is capable of accurately predicting an occupant's thermal insulation with a confidence interval of approximately ±0.3 and a mean prediction error of 0.2.