IoT-Enabled Camera-Based Eye Tracking and Data Analysis for Real-Time Detection of Emotional State and Attention Deficit

Psychological conditions such as obsessive-compulsive disorder (OCD), depression, and attention deficits are frequently associated with distinctive patterns of visual attention and emotional reactivity. [1], [2], [3] This study presents an innovative IoT-enabled eye-tracking system designed to analyze real-time gaze behavior, pupil dilation, and attentional bias in university students exposed to affectively charged stimuli. The system integrates high-resolution camera infrastructure with open-source image processing algorithms to extract temporal and spatial gaze metrics. The experimental protocol follows a two-phase paradigm: an initial free-viewing task, followed by a selective attention task using paired positive and OCD-relevant visual stimuli. Participants are stratified based on OCD symptom severity. Findings are interpreted within the frameworks of attentional maintenance and disengagement in OCD [3], [1], [2], and sustained attention in depression [4], [9], [12]. The study also explores cognitive processing patterns of university students in multichannel learning conditions to support personalized interventions [5], [6]. By analyzing eye-tracking outputs - including fixation duration, saccadic patterns, and pupil dynamics - the proposed system contributes to the detection of subclinical anxiety and mood dysregulation [7], [8], [11]. The incorporation of validated visual stimuli, such as the International Affective Picture System (IAPS) [10], ensures experimental standardization and emotional consistency across participant groups. This approach provides a scalable, low-cost tool for educational institutions and clinical researchers aiming to develop personalized digital mental health strategies.