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@article{201079,
        author = {Ms. Nishigandha Somanth Gavali and Prof. Swati S. Hinge},
        title = {GazeTrack: An AI-Based Multimodal Assistive System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2708-2713},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201079},
        abstract = {Human Computer Interaction (HCI) is evolving toward more natural and touchless technologies that improve both usability and accessibility. This paper presents GazeTrack, a personal AI-driven assistive system that allows users to control computers using hand gestures, eye tracking and voice commands, eliminating the need for traditional devices such as keyboards and mice. A standard webcam captures real-time hand movements, which are analyzed using computer vision techniques to recognize gestures for cursor movement, clicking, scrolling, and drag-and-drop actions.
In addition, voice commands enable users to perform system operations without physical interaction. The system ensures smooth and responsive performance by combining gesture and speech inputs effectively. GazeTrack is particularly beneficial for individuals with physical disabilities, as it reduces effort and supports contactless interaction. Experimental results show high accuracy, quick response, and consistent performance, making it a practical alternative to conventional input methods.},
        keywords = {Multimodal HCI, Gesture Recognition, Voice Control, Virtual Mouse, AI Interface, Real-Time Interaction},
        month = {May},
        }