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Inferring Clothing Insulation Levels Using Mechanisms of Heat Transfer

Published: 26 September 2017 Publication History

Abstract

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 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. Since humans dress to target a comfortable thermal sensation, it is reasonable to assume that clothing is an important measure of current thermal sensation. To this end, 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. The proposed inference algorithm builds upon theories of body heat transfer and is corroborated by empirical data. SiCILIA was tested in a vehicle with a passenger-controlled HVAC system. Experimental results show that the algorithm is capable of accurately predicting an occupant’s thermal insulation with a mean prediction error of 0.07clo.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 13, Issue 4
November 2017
290 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3139355
  • Editor:
  • Chenyang Lu
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 26 September 2017
Accepted: 01 May 2017
Revised: 01 March 2017
Received: 01 June 2015
Published in TOSN Volume 13, Issue 4

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Author Tags

  1. Clothing insulation
  2. heat transfer
  3. sensor

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  • Natural Sciences and Engineering Research Council of Canada

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