“VISION AND EMOTION: LEVERAGING EYE TRACKING DATA FOR MENTAL HEALTH ASSESSMENT”

Abstract: Mental health disorders such as depression and anxiety are often underdiagnosed because current assessments rely heavily on self-report questionnaires and clinical interviews, which are subjective, time-consuming, and difficult to scale. Recent studies show that eye movement behaviour- such as fixation patterns, saccade dynamics, and gaze allocation to emotional stimuli-can serve as objective digital biomarkers for mental health conditions [1], [2]. This paper presents VISION AND EMOTION, a real-time mental health assessment system that leverages eye-tracking data captured using a standard camera. The system records gaze trajectories while users interact with carefully designed visual tasks (emotionally valence images, reading tasks, and attention-switching trials) and extracts features such as fixation duration, saccade amplitude, blink rate, and gaze distribution across regions of interest. These features are used to train a machine learning classifier (Support Vector Machine) to distinguish between Normal and At-Risk mental health states. The proposed framework is lightweight, non-invasive, and deployable on commodity hardware without dedicated infrared eye trackers. Experimental evaluation demonstrates that the system can achieve promising classification performance with low latency, enabling near real-time feedback suitable for preliminary mental health screening. By combining eye-tracking analytics with machine learning, the system contributes toward scalable, objective, and cost- effective digital mental health tools that can complement traditional clinical assessments [2].

Keywords: Eye Tracking, Mental Health Assessment, Depression Detection, Gaze Analysis, Digital Biomarkers, Machine Learning, Real-Time Monitoring.