Depression detection using virtual avatar communication and eye tracking system

Abstract

Globally, depression is one of the most common mental health issues. Therefore, finding an effective way to detect mental health problems is an important subject for study in human-machine interactions. In order to examine the potential in using a virtual avatar communication and eye-tracking system to identify people as being with or without depression symptoms, this study has devised three research aims; 1) to understand the effect of different types of interviewers on eye gaze patterns, 2) to clarify the effect of neutral conversation topics on eye gaze, and 3) to compare eye gaze patterns between people with or without depression. Twenty-seven participants - fifteen in the control group and twelve in the depression symptoms group - were involved in this study and they were asked to talk to both a virtual avatar and human interviewers. Gaze patterns were recorded by an eye-tracking device during both types of interaction. The experiment results indicated significant differences in eye movements between the control group and depression symptoms group. Moreover, the identified differences were more pronounced when people with in depressed symptoms group were talking about neutral conversation topics rather than negative topics.