Association of facial infrared thermography with thermal comfort and cognitive performance in summer indoor environments
- 1School of Civil Engineering and Transportation, Guangzhou University, Guangzhou, 510006, China
- 2Corresponding author.
Abstract
Although the mechanisms linking localized skin temperatures (e.g., limbs, torso) to thermal comfort have been well-established in indoor environmental studies, the differential impacts of regional facial temperatures (e.g., nose, chin) on thermal sensation and cognitive performance remain underexplored. This study conducted simulated experiments in a Guangzhou conference room during summer, varying temperatures within the 22–30°C range, with 31 healthy participants completing subjective thermal comfort evaluations and cognitive tests. Infrared facial thermography captured skin temperatures across five regions (forehead, left/right cheeks, nose, chin) to systematically investigate their associations with Comprehensive Thermal Comfort (CTC) and cognitive performance. Machine learning-based thermal comfort assessment models were developed and validated through hyperparameter optimization and cross-validation. Key findings include: All facial regions showed significant correlations with CTC, with the cheeks (2.53°C variation) and nose (3.46°C variation) demonstrating superior monitoring utility due to their larger thermal amplitudes compared to the forehead (0.89°C variation). Nose temperature exhibited a significant nonlinear relationship with cognitive performance, peaking at 35.51°C for optimal cognitive efficiency. The Random Forest model demonstrated that integrating regional facial temperatures with ambient air temperature significantly enhanced prediction accuracy, particularly those of the right cheek, left cheek, and nose, achieving accuracy of 0.92, 0.91, and 0.90 respectively. These results validate the prioritized use of nose and cheek temperatures in personalized thermal comfort models. Integrating non-contact facial thermography with smart HVAC systems could enable physiology-driven indoor climate control, balancing energy efficiency with cognitive performance enhancement.
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BibTeX
@article{he2026association,
title = {{Association of facial infrared thermography with thermal comfort and cognitive performance in summer indoor environments}},
author = {Junkang He and Yanchen Liu and Huijun Wu and Yanni Liu},
journal = {Energy and Built Environment},
year = {2026},
month = oct,
doi = {10.1016/j.enbenv.2025.10.003},
url = {https://doi.org/10.1016/j.enbenv.2025.10.003},
note = {CC BY},
}
RIS
TY - JOUR
TI - Association of facial infrared thermography with thermal comfort and cognitive performance in summer indoor environments
AU - Junkang He
AU - Yanchen Liu
AU - Huijun Wu
AU - Yanni Liu
PY - 2026
DA - 2026/10/30
JO - Energy and Built Environment
DO - 10.1016/j.enbenv.2025.10.003
UR - https://doi.org/10.1016/j.enbenv.2025.10.003
AB - Although the mechanisms linking localized skin temperatures (e.g., limbs, torso) to thermal comfort have been well-established in indoor environmental studies, the differential impacts of regional facial temperatures (e.g., nose, chin) on thermal sensation and cognitive performance remain underexplored. This study conducted simulated experiments in a Guangzhou conference room during summer, varying temperatures within the 22–30°C range, with 31 healthy participants completing subjective thermal comfort evaluations and cognitive tests. Infrared facial thermography captured skin temperatures across five regions (forehead, left/right cheeks, nose, chin) to systematically investigate their associations with Comprehensive Thermal Comfort (CTC) and cognitive performance. Machine learning-based thermal comfort assessment models were developed and validated through hyperparameter optimization and cross-validation. Key findings include: All facial regions showed significant correlations with CTC, with the cheeks (2.53°C variation) and nose (3.46°C variation) demonstrating superior monitoring utility due to their larger thermal amplitudes compared to the forehead (0.89°C variation). Nose temperature exhibited a significant nonlinear relationship with cognitive performance, peaking at 35.51°C for optimal cognitive efficiency. The Random Forest model demonstrated that integrating regional facial temperatures with ambient air temperature significantly enhanced prediction accuracy, particularly those of the right cheek, left cheek, and nose, achieving accuracy of 0.92, 0.91, and 0.90 respectively. These results validate the prioritized use of nose and cheek temperatures in personalized thermal comfort models. Integrating non-contact facial thermography with smart HVAC systems could enable physiology-driven indoor climate control, balancing energy efficiency with cognitive performance enhancement.
SN - 2666-1233
ER -