Electrodermal activity and heart rate variability-driven personal thermal comfort prediction and explanation

Understanding personal thermal comfort (PTC) is a complex process requiring the prediction of individual satisfaction with a reasonable explanation. However, the explanation goes beyond predictive ability, and the implications of the interaction of physiological and physical factors need to be investigated. Herein, we address these issues with a new approach based on physiological measurement using electrodermal activity (EDA) and heart rate variability (HRV) for prediction and explanation. Feature engineering is proposed to decompose EDA into skin conductance responses and skin conductance levels (SCL) and HRV into low frequency (LF), high frequency (HF), and their ratio (LF/HF ratio). The classification algorithms are used to model the PTC prediction, and multiclass logistic regression (LR) encodes the explanation of predicted results. The predictive effectiveness-based F1-scores of artificial neural networks, K-nearest neighbor, and support vector machine achieve 85 %, 93 %, and 95 %, respectively. Multiclass LR-based odds ratio (OR) investigates the strength of the explanation and highlights that SCL and LF are outstanding explainable features, of which "1" is excluded from the OR sensitivity range. This highlights that the proposed approach based on EDA and HRV helps accomplish predictive and explainable abilities and can be used for future intelligent systems.