Automated Smart Home Assistive System Implementation for Physically Impaired Community Using Eye Gestures

This work introduces an innovative solution for enhancing home automation. By leveraging components such as a laptop webcam, Arduino Uno microcontroller, OpenCV in Python, and MediaPipe, this system enables the control of home appliances through the detection of users' eyeball movements. The project combines computer vision and machine learning techniques to accurately identify eye movements and interpret them as commands for various household devices. Users can effortlessly interact with their appliances, such as lighting, entertainment systems, or climate control, without the need for physical interfaces or voice commands. This novel approach not only enhances convenience but also addresses accessibility issues for individuals with physical disabilities. The integration of these technologies showcases the potential of human-computer interaction in the context of smart homes, offering a glimpse into the future of automation and accessibility.