Revolutionizing Human-Machine Interaction through Hand Gesture Recognition in 6G-Enabled Metaverse Environments

The advent of 6G networks and the emergence of the metaverse have catalyzed innovative approaches to human-machine interaction. In this paper, we present a novel GestureRecognition model empowered by deep learning, designed to revolutionize gesture-based communication within the metaverse. Leveraging a meticulously curated dataset of near-infrared hand images captured by the Leap Motion sensor, our model excels in recognizing ten distinct hand gestures with unprecedented accuracy. We achieved remarkable results, showcasing an average accuracy of 99.75% and F1-scores of 99% for each gesture. The classification report and confusion matrix provide a comprehensive evaluation of our model's performance. These results underscore the model's robustness and precision, reaffirming its potential to enhance user experiences in the metaverse through intuitive and natural hand gesture recognition. This work contributes to the convergence of 6G networks and the metaverse, promising seamless and immersive interactions in the digital realm.