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Title: Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors

Abstract

Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [2]
  1. Bosch Research & Technology Center
  2. Carnegie Mellon University
  3. National University of Singapore
  4. Technical University of Munich
Publication Date:
Research Org.:
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
Contributing Org.:
Bosch Research & Technology Center National University of Singapore Carnegie Mellon University Technical University of Munich
OSTI Identifier:
1577341
DOE Contract Number:  
EE0007682
Resource Type:
Conference
Journal Name:
DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis
Additional Journal Information:
Conference: SenSys '19: The 17th ACM Conference on Embedded Networked Sensor Systems , New York, NY, November 10 - 10, 2019
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Datasets, Thermal Comfort, Biometrics, Depth Data, Body Shape Estimation

Citation Formats

Munir, Sirajum, Francis, Jonathan, Quintana, Matias, von Frankenberg, Nadine, and Berges, Mario. Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors. United States: N. p., 2019. Web. doi:10.1145/3359427.3361915.
Munir, Sirajum, Francis, Jonathan, Quintana, Matias, von Frankenberg, Nadine, & Berges, Mario. Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors. United States. doi:10.1145/3359427.3361915.
Munir, Sirajum, Francis, Jonathan, Quintana, Matias, von Frankenberg, Nadine, and Berges, Mario. Sun . "Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors". United States. doi:10.1145/3359427.3361915. https://www.osti.gov/servlets/purl/1577341.
@article{osti_1577341,
title = {Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors},
author = {Munir, Sirajum and Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Berges, Mario},
abstractNote = {Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.},
doi = {10.1145/3359427.3361915},
journal = {DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

Conference:
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Works referenced in this record:

OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
conference, January 2019

  • Francis, Jonathan; Quintana, Matias; von Frankenberg, Nadine
  • Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation - BuildSys '19
  • DOI: 10.1145/3360322.3360858

Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms
conference, April 2017

  • Munir, Sirajum; Arora, Ripudaman Singh; Hesling, Craig
  • 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)
  • DOI: 10.1109/RTAS.2017.8