Evaluation of variations in autonomic nervous system's activity during the day based on facial thermal images using independent component analysis

Abstract

An imbalance of the circadian rhythm due to an irregular lifestyle leads to autonomic dystonia. The long-hour variations of the physiological indices such as core temperature and heart rate variability, associated with the autonomic nervous activity, have been focused. Additionally, the relationships between these physiological indices and facial skin temperature have been reported. The objective of this study is to identify the day-long variable components of facial thermal images (FTIs) to evaluate the corresponding variations of autonomic nervous activity by non-contact measurement. In this study, the measured FTIs were subjected to independent component analysis (ICA). Additionally, multiple regression analysis was performed to estimate the relationships between independent components, extracted from FTIs, and other psychophysiological indices, associated with autonomic nervous activity. As a result, the facial day-long variable components, representing the reproducible rhythm through multiple days, were identified by applying ICA to several different combinations of FTIs. Moreover, these components were associated with the axillary temperature. Therefore, the long-period variations, associated with autonomic nervous activity, could be evaluated from the facial skin temperature.