Control System Based on EOG for People with Disabilities

Electrooculography (EOG)-based systems present a promising solution for individuals with severe motor disabilities by enabling hands-free control through eye movement signals. Despite advances in assistive technology, a knowledge gap remains in developing robust, user-friendly, and real-time EOG-controlled human–machine interfaces (HMIs). This study introduces a novel EOG-based control system composed of wearable sensors, a microcontroller-based processing unit, and machine learning algorithms to classify eye movements. The system was tested with both healthy individuals and patients with disabilities, demonstrating high accuracy in controlling devices such as robotic arms, wheelchairs, and smart home appliances. Results indicate the system achieves over 85% recognition accuracy with low false-positive rates. The findings underscore the potential of EOG-driven HMIs to significantly improve autonomy, communication, and quality of life for users with physical impairments, with future integration of artificial intelligence and wireless technologies promising even broader applicability.