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@article{208570,
        author = {Shweta Rajkumar Yadav and Neha Deepak Bhalerao},
        title = {AI-Assisted Eye-Controlled Human–Computer Interaction and IoT-Based Home Automation for People with Disabilities and Paralysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {495-506},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208570},
        abstract = {People with severe motor disabilities and paralysis often face difficulties in operating conventional computers and communicating independently. This research presents an artificial intelligence-assisted eye-controlled human-computer interaction system that provides an alternative input mechanism using eye movement, slight head movement, and intentional blinking. A computer vision module using the built-in laptop webcam and OpenCV with MediaPipe Face Mesh detects iris movement and slight head movement and translates the detected movements into mouse cursor movement, while a blink sensor connected to an Arduino enables hands-free selection. The system incorporates a virtual keyboard with predictive word and sentence assistance to reduce the effort required for eye-based typing. A text-to-speech facility converts typed messages into audible speech. Emergency short message service alerts, caretaker calling, and a local buzzer-based emergency alert are integrated to provide additional assistance. An ESP8266-based home automation interface enables users to control appliances such as lights, fans, and windows. The proposed system was developed as a low-cost prototype integrating assistive communication, computer interaction, emergency assistance, and environmental control into a unified platform for individuals with motor disabilities.},
        keywords = {Assistive technology, eye tracking, home automation, human-computer interaction.},
        month = {September},
        }