TSVNet: Combining Time-Series and Opportunistic Sensing by Transfer Learning for Dynamic Thermal Sensation Estimation

Abstract

The development of sensing technologies has enabled the estimation of human thermal sensation, which helps efficient heating, ventilation, and air conditioning control. Recently, machine learning techniques using physiological and environmental sensors have been proposed. However, in most existing literature, the personal thermal sensation is estimated using only opportunistic data as features, and evaluations are conducted in a static environment. In real situations, we are exposed to a dynamic environment, owing to human mobility and changes in airflow. Therefore, it is necessary to estimate personal thermal sensation in a dynamic surrounding environment. In this study, we propose TSVNet, a deep-learning-based method reflecting time-series changes to address the dynamic environment. We collected data for a total of 123 days, which included the dynamic environment data from 21 subjects, for the evaluation. The results indicate that our method improves F1-score by 5.8% compared with a baseline method. We also design a data balancing method for regression problems on imbalanced datasets, including time-series data. In addition, the result of the lookback time evaluation shows that the use of physiological information in the previous 10 minutes improves the performance of the method.