Gesture-Driven Home Automation System

This project addresses the critical challenge of making home automation accessible and inclusive for individuals with physical disabilities and visual impairments. Existing home automation methods, such as voice control, mobile apps, and timers, exhibit shortcomings that hinder their effectiveness for this demographic. To overcome these limitations, the project shows the development of a gesture-based home automation system. This system integrates gesture recognition and security functionality. It operates in two stages: user authentication through face recognition and gesture-based appliance control. A Convolutional Neural Network (CNN) processes webcam-captured gestures, with signals relayed to an Arduino via Pyfirmata for appliance activation. A model was trained where it could detect the human hand(s) and more soshow the 20 landmarks that media pipe offers. The system worked well in controlling various home appliances using gestures. Through this innovative approach, the project contributes to the advancement of accessible smart home technology, fostering independence and inclusivity for users with diverse abilities.