A Novel Approach to Perform Mouse Operations Virutally by using Intelligent Hand Gesture Recoginition Methodology

Hand gesture recognition has emerged as a vital aspect of human-computer interaction, offering intuitive, touchless interfaces for diverse applications. This study presents a novel approach to virtual mouse operations using intelligent hand gesture recognition, enabling seamless human-computer interaction. The system employs a hybrid Vision Transformer (ViT) and Gated Recurrent Unit (GRU) model to achieve robust gesture recognition by combining spatial and temporal feature extraction. A diverse dataset of hand gestures was used, ensuring adaptability across different lighting conditions, user preferences, and gestures. Preprocessing techniques such as noise reduction, normalization, and data augmentation improved the system's reliability. The proposed model achieved an accuracy of 96.8%, outperforming existing models like YOLO-BiLSTM and CapsNet-TCN, with F1-scores of 94.7% and 95%, respectively. Gesture-wise accuracy showed high performance for cursor movement (97.5%) and drag-and-drop (96.7%), while scrolling gestures maintained consistent accuracy above 94.8%. Additionally, the system demonstrated a low latency of 78 ms, enabling real-time performance. User testing reported an average satisfaction score of 4.7 out of 5, highlighting its practicality. This innovative approach has significant applications in accessibility, gaming, and smart environments, making it a promising tool for intuitive human-machine interaction.