Exploring the role of skin temperature in thermal sensation and thermal comfort: A comprehensive review

Abstract

The role of skin temperature as a determinant of human thermal sensation and comfort has gained increasing recognition, prompting a need for a systematic review. This review examines the relationship between skin temperature and thermal sensation, synthesizing insights from 172 studies published since 2000. It uniquely focuses on the indispensable roles of local and mean skin temperatures, a perspective not comprehensively explored in previous literature. The review reveals that the most common measurement points for skin temperature are the face and hands, attributed to their higher thermal sensitivity and the practical ease of measurement. It establishes a clear linear relationship between mean skin temperature and user thermal sensation, though affected by the choice of measurement locations and number of points. A notable finding is the varying impact of local skin temperature on overall thermal sensation in changing environments, with local heating less influential than cooling. The review also uncovers demographic variations in thermal sensation, strongly influenced by differing skin temperatures across age groups, genders, and climatic regions. For example, elderly populations exhibit a decreased temperature sensitivity, especially towards warmth. Gender differences are also significant, with females experiencing higher skin temperatures in warmer environments and lower in colder ones. Machine learning (ML)-based methods, particularly those using classification tree and support vector machine (SVM) techniques, are increasingly used to predict thermal sensation and comfort by leveraging skin temperature data. While ML methods are prevalent, statistical regression-based approaches offer valuable empirical insights. Thermo-physiological model-based methods provide reliable results by incorporating detailed skin temperature dynamics. The review highlights a gap in understanding the influence of gender, age, and regional differences on thermal comfort across various environments. The study recommends conducting more detailed experiments to examine the impact of these factors more closely. It also suggests integrating individual demographic variables into ML models to personalize thermal comfort predictions.