Modeling Of Child Stress-State Identification Based On Biometric Information In Mobile Environment

A technology must be developed to automatically identify extreme stress states of children who cannot properly express their emotions when recognizing dangerous situations, which threaten the safety of children, in real time. This study presents a stress-state identification model for children based on machine learning, biometric data, a smart band for collecting biometric data, and a mobile application for monitoring the stress state of the child classified. In addition, through an experiment comparing a dataset using only voice data and a dataset using both voice and heart rate data, we aimed to verify the effectiveness of the combination of the two biosignal datasets. As a result of the experiment, the SVM model showed the highest performance with an accuracy of 88.53% for the dataset using both voice data and heart rate data. The results of this study presented strong implications for the possibility of automating the stress-state identification of a child, and it is expected that the developed method can be used to take preventive measures for dangerous situations to children.