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@article{205960,
        author = {Mounish S and Dr. Nandhini and Logeshwaran M and Mano Vignesh and Mohammed Irfan},
        title = {Real-Time Eye Gaze Typing and Control of Activities for Individuals with Severe Motor Disabilities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2869-2875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205960},
        abstract = {Eye movements are vital in human-computer interaction (HCI) as they reveal attention and mental state. Video-based gaze tracking has gained importance in recent years due to its non-intrusive and accurate nature. This paper proposes a cost-effective gaze-tracking system using iris movement with the MediaPipe face mesh model. A five-point calibration and regression approach are applied to predict gaze points, while z-index tracking supports real-time re-calibration and compensates for small head shifts. The system costs under $25, depending on the camera, and was tested with thirteen participants. It performs reliably under different lighting and distances, with moderate impact from glasses. The average frame processing time is 0.047 seconds, achieving 1.12° accuracy with head movement and 1.3° without, showing its potential as a practical and efficient HCI tool.},
        keywords = {Gaze tracking, Iris movement tracking, Media Pipe face mesh, 5-point calibration, Multiple regression, Z-index tracking.},
        month = {July},
        }