Machine-learning approach for predicting individual's thermal comfort and thermal sensation in outdoor environments

In this study, we aimed to evaluate the accuracy of wrist-mounted wearable sensors in measuring and predicting individuals’ thermal comfort sensations in transitional and outdoor environments. To achieve this, we combined mobile measurements, wearable devices, and surveys to generate a reliable dataset from outdoor settings. We assessed the universal thermal climate index (UTCI) and wrist-mounted wearable data in relation to thermal comfort votes (TCV) and thermal sensation votes (TSV). Our findings revealed that UTCI strongly correlates with individuals’ thermal comfort sensations and serves as a reliable indicator of outdoor thermal comfort, particularly in Sydney's outdoor environments. We observed that wrist air temperature demonstrates a correlation pattern with TCV similar to that of UTCI and exhibits an even stronger correlation with TSV. This finding suggests that wrist air temperature can serve as an effective indicator of thermal comfort sensations in the absence of UTCI. Using a random forest machine learning algorithm, we developed a prediction model for UTCI based on wrist-mounted sensor data. The results demonstrated the potential of wrist-mounted sensors to accurately predict UTCI, further validating their effectiveness in assessing outdoor thermal comfort. Furthermore, we utilized wearable data to directly develop prediction models for TCV and TSV using the same machine-learning approach. Feature importance analysis revealed that mean radiant temperature, wind speed, and wrist air temperature significantly influence the prediction models, emphasizing that thermal conditions play a critical role in these predictions.