International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Gesture-controlled Home Appliances for Disable People Using CNN algorithm Mrs. Sneha Sankhe1,Mr. Mohd Sami Niyazuddin Gawandi2, Mr. Harsh Gharat3, Mr. Rugved Patil4 1,2,3Information Technology, Theem college of Engineering, Mumbai, India 1Professor, Department of Information Technology, Theem College of Engineering, Boisar, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------1. INTRODUCTION Abstract - In an effort to enhance the quality of life
and autonomy of disabled individuals, this research presents a novel approach to designing gesture-controlled home appliances through the utilization of a Convolutional Neural Network (CNN) algorithm. The integration of technology into the lives of disabled individuals has the potential to significantly mitigate the challenges they encounter in performing everyday tasks. Leveraging the capabilities of CNNs, which have demonstrated remarkable proficiency in image recognition tasks, this study aims to develop a system that interprets and responds to specific gestures executed by individuals with disabilities. The proposed system harnesses the power of deep learning to accurately classify and recognize a diverse range of gestures, facilitating seamless interaction with household appliances. To realize this, a comprehensive dataset comprising a wide array of gestures relevant to household activities will be curated and utilized for training the CNN model. The model will undergo rigorous optimization and validation processes to ensure its reliability and generalizability. By enabling gesture-based control of home appliances, individuals with disabilities can exercise greater autonomy and independence in their domestic environments. The system holds the potential to empower users by affording them the ability to operate appliances such as lights, fans, and home entertainment systems through intuitive gestures, thereby eliminating the need for traditional physical interfaces. This not only enhances their sense of agency but also fosters a sense of inclusion and equality within their communities. Moreover, gesture-controlled appliances offer a user-friendly and intuitive means of interaction, potentially eliminating barriers posed by traditional control mechanisms that may require intricate motor skills. The implementation of this system necessitates a multi-faceted approach encompassing data collection, preprocessing, model architecture design, training, and deployment. The CNN model's accuracy and robustness will be systematically evaluated through a battery of tests, encompassing scenarios of varying complexity and environmental conditions. Furthermore, considerations will be given to the adaptability of the system, ensuring compatibility with a range of home appliances and minimal latency in gesture recognition. Ethical dimensions of the technology, including user privacy and data security, will be addressed through stringent measures to safeguard sensitive information.
The overview involves the development of a gesture-controlled system for home appliances, catering specifically to individuals with disabilities. This innovative approach harnesses the power of Convolutional Neural Networks (CNNs) to interpret and respond to various gestures, enabling seamless interaction with household devices. By utilizing deep learning techniques, the system aims to enhance user independence and empowerment, offering an intuitive and inclusive means of controlling everyday appliances. The project encompasses several crucial stages, including data collection, preprocessing, CNN model architecture design, training, validation, and deployment. Ethical considerations, privacy, and data security are integral components of the system design The need for home automation is becoming increasingly evident as modern living continues to evolve. Home automation offers a solution to a variety of pressing challenges and demands in our daily lives. First and foremost, it enhances convenience and comfort by allowing homeowners to control and manage various aspects of their living space with unprecedented ease. From adjusting lighting and temperature to locking doors and monitoring security, home automation systems streamline the control of the home environment. Beyond convenience, home automation also plays a crucial role in enhancing energy efficiency and sustainability. Smart thermostats and lighting systems can optimize energy consumption, ultimately reducing utility bills and carbon footprints. For those with disabilities or limited mobility, home automation represents a means of regaining independence, as it enables them to manage their surroundings without physical exertion. Moreover, the integration of home security features, such as video doorbells and surveillance cameras, fosters a heightened sense of safety and peace of mind.
2. Literature Survey The concept of gesture-controlled technology has gained significant traction in recent years, driven by advancements in artificial intelligence and machine learning. Gesture recognition, a subset of human-computer interaction, involves interpreting human gestures and translating them into commands that computers or devices can understand and execute. This technology has
Key Words: Hand Gesture, CNN, Hardware
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