Sang Won Bae, a professor at Stevens Institute of Technology, is developing smartphone applications powered by artificial intelligence that could non-invasively detect early signs of depression. These applications analyze eye movements and facial expressions to identify potential emotional changes. Two key tools in his research are PupilSense and FacePsy.

PupilSense captures snapshots of users’ pupils while they interact with their phones, measuring pupillary responses, which previous studies have linked to depressive episodes. In initial tests, PupilSense demonstrated 76% accuracy in detecting potential symptoms of depression. On the other hand, FacePsy analyzes facial expressions and head movements to detect non-verbal signals associated with depression. These apps are designed to run in the background, constantly detecting signs without being intrusive or compromising user privacy, as images are deleted immediately after analysis.

This innovative approach could revolutionize mental health monitoring, offering a simple and accessible tool to detect depression, enabling early intervention without the need for additional devices. Bae’s research is still in its early stages but shows promising results, with the potential to transform mental health diagnosis and monitoring in the future