Interactive Projection Technology using Hand Gesture Recognition with Attention Mechanism and ResNet

This study aims to simplify human-computer interaction and make it more user-friendly by incorporating gesture recognition. Individuals can interact with machines without physical instruments like keyboards or laser pens with gestures. Hand gesture recognition is a technology that can accurately detect hand movements in real-time video. This work proposes a novel approach to developing interactive projection technology by incorporating ResNet and an attention mechanism for hand gesture recognition. ResNet, a deep convolutional neural network architecture, is used to extract high-level features from RGB and depth images of hand gestures. The attention mechanism is employed to enhance the spatial and temporal features of the hand gestures, resulting in improved accuracy. The main objective of this work is to demonstrate hand gestures as an intuitive and practical way to control devices without physical touch, focusing on training gestures for page navigation and scrolling operations in a PowerPoint slideshow. Using hand gestures is considered a more practical strategy than a laser pointer, as the hand is more noticeable and can better grab the audience's attention. The proposed approach using ResNet and attention mechanism shows promising results for hand gesture recognition, paving the way for more natural and engaging human-computer interactions in the future.