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

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 significant 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, especially classification tree-based and support vector machine (SVM) techniques, dominate in predicting thermal sensation and comfort, 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 identifies a gap in understanding how gender, age, and regional differences influence thermal comfort in diverse environments. The study recommends conducting more nuanced experiments to dissect the impact of these factors and proposes the integration of individual demographic variables into ML models to personalize thermal comfort predictions.