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Off-the-shelf wearable sensing devices for personalized thermal comfort models: A systematic review on their use in scientific research

Journal

JOURNAL OF BUILDING ENGINEERING
Volume 70, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.jobe.2023.106379

Keywords

Artificial intelligence; Deep learning; Machine learning; Multi-domain comfort; Physiological signals

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Human thermal comfort is influenced by objective variables related to the environment and subjective variables related to physiological conditions. The use of off-the-shelf wearable devices has greatly facilitated the investigation of these subjective variables. This study provides a detailed framework for utilizing off-the-shelf wearable devices in thermal comfort investigations.
Human thermal comfort depends on objective variables-related to the environment-and to subjective variables, related to physiological conditions. While the former are relatively easy to be measured, the latter are difficult to be investigated since differ from person to person and they are characterized by sudden variations over time. The recent spread of off-the-shelf wearable devices for monitoring bio-signals has considerably facilitate this challenging task. The aim of this work is to provide a detailed framework about the use of off-the-shelf wearable devices for thermal comfort investigations. A systematic review of 35 scientific papers-selected over 302 results from the initial database query-was performed. The results highlight that wristbands (mainly, Empatica E4 and Fitbit), headbands (i.e., Muse 2), chest bands (mainly, BioHarness 3.0 and Polar H7), miniature data loggers (i.e., iButton), and activity sensors (i.e., Move 3) were the off-the-shelf devices whose use is predominant in thermal comfort investigations. Those devices were adopted for different purposes, namely finding correlations between physiological signals and thermal sensations, training and/or validating thermal comfort models, improving data acquisi-tion, and controlling HVAC systems. The proposed framework could represent a solid background for future investigations which should focus on two main research streams. The first one should aim at strengthening the knowledge about statistical correlations between thermal sensations and physiological signals, as well as defining standardized procedures for the model development and validation. The second research stream should aim at integrating off-the-shelf wearable devices and personalized thermal comfort models into HVAC control systems.

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