research-article

Personal thermal perception models using skin temperatures and HR/HRV features: comparison of smartwatch and professional measurement devices

Authors:

Marcel Schweiker,

Robert DürichenAuthors Info & Claims

ISWC '19: Proceedings of the 2019 ACM International Symposium on Wearable Computers

Pages 96 - 105

https://doi.org/10.1145/3341163.3347737

Published: 09 September 2019 Publication History

Abstract

In order to detect a person's individual momentary thermal sensation and comfort, an increased number of models are developed that involve physiological data, especially skin temperatures and features based on the heart activity. In this paper, we investigate the feasibility of Machine Learning (ML) models which include physiological data based on two data sources: (1) a smartwatch and (2) a portable chest belt device. Further, we investigate the difference between thermal sensation and comfort votes and propose a new combined ground truth label. Data were collected in lab-like studies in a single-bed hotel room. Our study focuses on the detection of cold-induced thermal discomfort by varying the room air temperature between 24 °C and 20 °C. Results show that ML based approaches lead to accuracies up to 83.1% when using a chest belt device. Using a smartwatch, accuracies drop down to 79.8%. Our investigation shows that even though Heart Rate (HR) based features improve the prediction accuracy, the highest feature importances have distal skin temperature features.

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Cited By

Wang YWang YDuan WZheng YDuan P(2023)Joint Measurement of Thermal Discomfort by Occupant Pose, Motion and Appearance in Indoor Surveillance Videos for Building Energy SavingJournal of Circuits, Systems and Computers10.1142/S021812662450051833:03Online publication date: 7-Sep-2023
https://doi.org/10.1142/S0218126624500518
Beaudette EFoo EIslam Molla MJohnson KDupler EGagliardi NWoelfle HHalvey MDunne L(2022)Investigating User Experience of On-Body Heating Strategies in Indoor EnvironmentsErgonomics in Design: The Quarterly of Human Factors Applications10.1177/1064804622107872032:2(25-32)Online publication date: 20-Apr-2022
https://doi.org/10.1177/10648046221078720
Morresi NCasaccia SRevel G(2021)Metrological characterization and signal processing of a wearable sensor for the measurement of heart rate variability2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA52024.2021.9478713(1-6)Online publication date: 23-Jun-2021
https://dl.acm.org/doi/10.1109/MeMeA52024.2021.9478713
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Personal thermal perception models using skin temperatures and HR/HRV features: comparison of smartwatch and professional measurement devices

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cover image ACM Conferences

ISWC '19: Proceedings of the 2019 ACM International Symposium on Wearable Computers

September 2019

355 pages

ISBN:9781450368704

DOI:10.1145/3341163

General Chairs:
Katayoun Farrahi
University Of Southampton
,
Robert Harle
Cambridge University
,
Nicholas Lane
University Of Oxford And Samsung Ai

Copyright © 2019 ACM.

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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Publication History

Published: 09 September 2019

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UbiComp '19

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UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 9 - 13, 2019

London, United Kingdom

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Overall Acceptance Rate 38 of 196 submissions, 19%

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Cited By

Wang YWang YDuan WZheng YDuan P(2023)Joint Measurement of Thermal Discomfort by Occupant Pose, Motion and Appearance in Indoor Surveillance Videos for Building Energy SavingJournal of Circuits, Systems and Computers10.1142/S021812662450051833:03Online publication date: 7-Sep-2023
https://doi.org/10.1142/S0218126624500518
Beaudette EFoo EIslam Molla MJohnson KDupler EGagliardi NWoelfle HHalvey MDunne L(2022)Investigating User Experience of On-Body Heating Strategies in Indoor EnvironmentsErgonomics in Design: The Quarterly of Human Factors Applications10.1177/1064804622107872032:2(25-32)Online publication date: 20-Apr-2022
https://doi.org/10.1177/10648046221078720
Morresi NCasaccia SRevel G(2021)Metrological characterization and signal processing of a wearable sensor for the measurement of heart rate variability2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA52024.2021.9478713(1-6)Online publication date: 23-Jun-2021
https://dl.acm.org/doi/10.1109/MeMeA52024.2021.9478713
Morresi NCasaccia SSorcinelli MArnesano MUriarte ATorrens-Galdiz JRevel G(2021)Sensing Physiological and Environmental Quantities to Measure Human Thermal Comfort Through Machine Learning TechniquesIEEE Sensors Journal10.1109/JSEN.2021.306470721:10(12322-12337)Online publication date: 15-May-2021
https://doi.org/10.1109/JSEN.2021.3064707
Fang YLim YOoi SZhou CTan Y(2020)Study of Human Thermal Comfort for Cyber–Physical Human Centric System in Smart HomesSensors10.3390/s2002037220:2(372)Online publication date: 9-Jan-2020
https://doi.org/10.3390/s20020372
Xie JLi HLi CZhang JLuo M(2020)Review on occupant-centric thermal comfort sensing, predicting, and controllingEnergy and Buildings10.1016/j.enbuild.2020.110392226(110392)Online publication date: Nov-2020
https://doi.org/10.1016/j.enbuild.2020.110392

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