Hand Gesture Driven Smart Home Automation Leveraging Internet of Things

Smart home automation systems require convenient and efficient user interface to control home appliances. Gesture recognition-based solutions offer flexibility to the users and play a crucial role in advancing human-computer interaction and immersive computing environments. This work proposes a novel solution leveraging deep learning techniques with attention mechanisms including self-attention tailored for processing 3D tensors derived from the gesture images. A set of hand gestures is defined, and the system is trained and optimized to meet the real time requirements in controlling devices. To improve the accuracy, the model is parallelly trained with dynamic learning to adaptively fuse with the classification module. The proposed modular architecture is implemented using Raspberry Pi with IoT devices for a typical home environment. The test result achieves gesture classification accuracy of98.24% and latency of about 0.2 seconds in real time control. The working model highlights a practical solution under ITU-T Recommendation J.1611 which deals with the functional requirements of a smart home and gateway.