Personal thermal comfort models with wearable sensors

A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual’s thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24%/78%/0.79 (Cohen’s kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21%/71%/0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in ...