Continuous hand gesture segmentation and acknowledgement of hand gesture path for innovative effort interfaces

Abstract

Human-computer interaction (HCI) has revolutionized the way we interact with computers, making it more intuitive and user-friendly. It is a dynamic field that has found it is applications in various industries, including multimedia and gaming, where hand gestures are at the forefront. The advent of ubiquitous computing has further heightened the interest in using hand gestures as input. However, recognizing continuous hand gestures presents a set of challenges, primarily stemming from the variable duration of gestures and the lack of clear starting and ending points. Our main objective is to propose a solution: the framework for “continuous palm motion analysis and retrieval” based on “Spatial-temporal and path knowledge”. Framework harnesses the power of cognitive deep learning networks (DLN), offering a significant advancement in the continuous hand gesture recognition domain. we conducted rigorous experiments using a diverse video dataset capturing hand gestures for boasting an impressive F-score of up to 0.99. The potential of our framework to significantly enhance the accuracy and reliability of hand gesture recognition in real-world applications.