New research has shown that the skin temperature on specific areas of the body is a strong indicator for how hot, cold or comfortable people feel. These findings could inform the design of wearable technology and smarter, more intuitive building climate control systems.
A new study by experts from the University of Nottingham's Faculty of Engineering shows that skin temperature, particularly at the face and hands, is closely tied to how comfortable or uncomfortable a person feels. Their findings have been published in the journal Energy and Built Environment.
Research in this area has been scattered and inconsistent, but this new study unites findings from 172 different studies since 2000, offering the most comprehensive analysis to date on the link between skin temperature and thermal sensation.
The researchers identified areas on the body that are not only highly sensitive to temperature changes but also easy to monitor, making them especially useful for real-world applications.
The researchers also found that local cooling - such as on the back or chest - can significantly improve comfort, while local heating has much less impact. This distinction has important implications for building climate control and personalised comfort technologies.
The study also highlights key demographic differences. Older adults, for example, tend to be less sensitive to warmth, potentially putting them at higher risk of overheating. Gender-related variations were also found, many studies report that women are more temperature sensitive across different environments, though findings are not always consistent. Climate background matters too - people from warmer regions respond to temperature differently than those from cooler ones, suggesting a need for more tailored approaches to thermal comfort.
Skin temperature tells us a great deal about whether people feel too hot, too cold, or comfortable indoors. By bringing together research from around the world, we've shown how this knowledge can help design safer, healthier and more sustainable spaces. Looking ahead, we see a future where smarter building technologies use this physiological data to automatically deliver comfortable, energy-efficient environments with minimal input from occupants.
The Nottingham team have also carried out feasibility research into using video cameras combined with deep learning to be able to predict people's comfort levels. This research offers a foundation for developing integrated, multi-parameter approaches to support more energy-efficient and intelligent built environments.
With the rise of AI, researchers are increasingly using machine learning to predict comfort levels from physiological signals such as skin temperature, reducing reliance on subjective surveys. This is especially useful for groups who cannot reliably express their comfort needs for example, elderly individuals, young children or people with dementia.
Dr Calautit continues: "This study lays the groundwork for smarter, more inclusive, and preventative approaches to managing thermal environments, helping reduce health risks and improve comfort for all."
