SMART ROOM LIGHT SYSTEM WITH HAND GESTURE CONTROL TO AID THE BLIND USING DEEP LEARNING

Abstract

The increasing adoption of smart home technologies necessitates the development of intuitive and user-friendly interfaces to enhance user experience and accessibility. This project focuses on the design and implementation of a smart room light system controlled by hand gestures, utilizing computer vision and a standard camera. By leveraging advanced machine learning algorithms for real-time gesture recognition, the system aims to provide an efficient, cost-effective, and natural interface for controlling lighting in smart home environments. The proposed system addresses key challenges identified in existing literature, such as the need for high accuracy, adaptability to diverse lighting conditions, and seamless integration with existing smart home infrastructure. This innovative approach not only enhances user interactivity and convenience but also promotes energy efficiency and accessibility, particularly benefiting individuals with disabilities or limited mobility. Through extensive testing and validation, the smart room light system demonstrates significant potential to transform user interaction within smart homes, paving the way for more intuitive and inclusive smart home solutions. However, the system has limitations, including potential difficulties in recognizing gestures under extremely low light conditions and the need for further optimization to handle a wider range of gestures.