Non-Invasive Multivariate Prediction of Human Thermal Comfort Based on Facial Temperatures and Thermal Adaptive Action Recognition

Accurately assessing human thermal comfort plays a key role in improving indoor environmental quality and energy efficiency of buildings. Non-invasive thermal comfort recognition has shown great application potential compared with other methods. Based on thermal correlation analysis, human facial temperature recognition and body thermal adaptive action detection are both performed by one binocular infrared camera. The YOLOv5 algorithm is applied to extract facial temperatures of key regions, through which the random forest model is used for thermal comfort recognition. Meanwhile, the Mediapipe tool is used to detect probable thermal adaptive actions, based on which the corresponding thermal comfort level is also assessed. The two results are combined with PMV calculation for multivariate human thermal comfort prediction, and a weighted fusion strategy is designed. Seventeen subjects were invited to participate in experiments for data collection of facial temperatures and thermal adaptive actions in different thermal conditions. Prediction results show that, by using the experiment data, the overall accuracies of the proposed fusion strategy reach 82.86% (7-class thermal sensation voting, TSV) and 94.29% (3-class TSV), which are better than those of facial temperature-based thermal comfort prediction (7-class: 78.57%, 3-class: 90%) and PMV model (7-class: 20.71%, 3-class: 65%). If probable thermal adaptive actions are detected, the accuracy of the proposed fusion model is further improved to 86.8% (7-class) and 100% (3-class). Furthermore, by changing clothing thermal resistance and metabolic level of subjects in experiments, the influence on thermal comfort prediction is investigated. From the results, the proposed strategy still achieves better accuracy compared with other single methods, which shows good robustness and generalization performance in different applications.