Realtime of Hand Gesture Recognition for Telerobotics Controller Based Leap Motion Using Random Forest

摘要

In the era of human-robot collaboration and the advancement of telerobotic systems, the research presented in this study explores the development of a real-time hand gesture recognition system for telerobotics control. Leveraging the potent Random Forest algorithm in tandem with the Leap Motion sensor, our investigation delves into the intricacies of enabling intuitive and precise remote control of robotic devices. The primary objective of this research is to establish a robust, responsive, and adaptable system capable of recognizing hand gestures in real time, thus facilitating enhanced human-robot interaction.The result of the study reveals that the Random Forest model, when employed for hand gesture recognition, attains remarkable accuracy and reliability, with an accuracy rate of 98.61%, a recall of 98.60%, precision at 98.31%, and an F1-Score of 98.22%. Comparative assessments with alternative methods, including Random Trees, Support Vector Machines (SVM), and Naive Bayes, demonstrate the superior performance of the Random Forest algorithm in this context.