부산대학교 도서관

This study investigates the invisible factors that influence the subjective satisfaction of sleep, which is a crucial factor in sleep-supporting systems. The effects of these factors were examined using a neural network (NN) model. A sensing bed system obtains the environmental and vital signs data with five-level evaluations. The relationships among over 300,000 collected data points were visualized and analyzed using the NN model. The results indicated that the temperature difference between the inside and outside of the bed affect the subjective satisfaction of sleep both during warm and cold periods. Furthermore, the NN models indicated that the respiratory frequency influences sleep satisfaction. In this study, both these factors and their effects on the subjective satisfaction of sleep were successfully investigated by utilizing the NN model.