Authors: Professor Sonali Dongare, Priyanshu Singh, Aditya Amup
Abstract: Motor impairments such as ALS, locked-in syndrome, and cerebral palsy severely limit an individual's ability to interact with digital systems using conventional input devices. This paper presents GazeSpeak, an AI-powered Eye Gaze Communication System that enables motor-impaired users to communicate through voluntary eye movements alone. The system extracts real-time gaze coordinates using OpenCV and MediaPipe, maps them onto interactive screen elements via a TensorFlow regression model, and integrates a transformer based NLP module for context-aware word prediction. A dwell-based selection mechanism activates interface targets without any physical input. Experimental evaluation across twenty participants demonstrates a gaze detection accuracy of 94.2%, end-to-end latency of 38ms, top-3 word prediction accuracy of 87.6%, and communication throughput of 10.6 WPM, with a System Usability Scale score of 84.4 confirming excellent user acceptance. The results establish GazeSpeak as an effective, open-source, and cost-accessible assistive communication platform for real-world deployment.
