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This study introduces a new method of home automation tailored to the needs of those who have trouble moving about. Using only your hands, you can turn on and off lights, fans, and other electrical devices. Providing individuals with mobility impairments with the means to autonomously manage their living area is the declared goal of the initiative, which aims to encourage greater independence and better their quality of life. The suggested system uses computer vision and machine learning techniques to decipher recorded hand motions seen through a camera interface. The system may be able to interpret specific hand movements as commands to operate different home appliances if it has learned to recognize them. We create and deploy a reliable system for the detection and categorization of gestures in real-time. Modern computer vision techniques, including Convolutional Neural Networks (CNNs) and algorithms for hand tracking, enable the system to accurately understand motion. Assigning certain commands to the recognized motions allows for the operation of smart home electrical products, such as lighting and ceiling fans. Plus, if the disabled person needs assistance, this device may notify the caregiver. We aim to enable people with impairments to live more independently and comfortably in their homes by giving this home automation system that is controlled by gestures. Accessibility and inclusion in smart home settings are significantly advanced by our effort, which leverages state-of-the-art technology and prioritizes user-centric design concepts.
Sakthy et al. (Fri,) studied this question.