HAND GESTURE RECOGNITION AND CURSOR CONTROL

HAND GESTURE RECOGNITION AND CURSOR CONTROL

Rishikesh Kharade1, Rajnandini Gitte2, Pratiksha Mundhe3, Mansi Patil4 , R.A. Patil5
1,2,3,4Student, Dept. of Computer Engineering, S.K.N.C.O.E. Vadgaon , Pune, Maharashtra, India (5Professor, Dept. of Computer Engineering, S.K.N.C.O.E. Vadgaon , Pune, Maharashtra, India)

Abstract - Due to their potential use in robotics, virtual reality, augmented reality, and human-computer interaction, hand gesture detection and cursor control systems have attracted a lot of attention lately. This study provides a thorough analysis of the cutting-edge methodology and techniques used in hand gesture recognition and cursor control. The study begins by outlining the core difficulties and demands of hand gesture detection systems, such as accuracy, real-time performance, and robustness to various environmental circumstances. It gives a general overview of the many sensing techniques, including wearables, cameras, and depth sensors, that are used to record hand movements. The report also examines the various hand tracking and feature extraction methods used to precisely analyse and decipher hand motions. The paper then explores the various machine learning techniques. algorithms for recognising gestures, such as convolutional neural networks and deep learning, as well as more established approaches like decision trees and support vector machines. It summarises recent developments in the area and evaluates the benefits and drawbacks of each strategy. The research also investigates the integration of cursor control mechanisms with hand gesture detection systems, allowing users to interact with computers and digital interfaces without making direct physical touch. It looks into a number of cursor control methods, including direct hand-to-cursor mapping, gesture-based instructions, and gesture-based object selection. The study also discusses assessment measures and datasets that are frequently used to compare gesture recognition and cursor control systems. It also discusses the difficulties of creating reliable and user-friendly technologies, , such as user adaption, scalability, and gesture ambiguity. The paper finishes with a discussion of new developments and potential lines of inquiry in the fields of cursor control and hand gesture recognition. It emphasises the potential uses in virtual reality, healthcare, industrial automation, and gaming while highlighting the need for additional study to overcome current issues and boost system efficiency. In conclusion, this work offers a thorough analysis of hand gesture recognition and cursor control systems, providing information on the underlying technologies, methods, and difficulties. For scientists, engineers, and professionals working on cutting-edge human-computer interaction systems, it is an invaluable resource.

Keywords — Sensing technologies, feature extraction,

Convolutional neural networks, object selection, industrial automation