A proposal for Hand gesture control applied to the KUKA youBot using motion tracker sensors and machine learning algorithms

Abstract

This paper presents a proposal for real-time hand gesture recognition for both dynamic and static gestures. For gesture detection, a dataset is obtained using capture sensors such as Leap Motion and Myo armband. The training process is managed by stages of data acquisition, preprocessing, feature extraction, and classification. In this context, in the classification stage, we determine the machine learning algorithms studies through mathematical model investigations. These algorithms allow to determine the real-time processing of hand gesture types and transfer them to a KUKA youBot for control. An application programming interface with ROS (Robot Operating System) is used for the communication process between the sensors and the robot. Finally, a hand gesture control environment is created after obtaining the training data set for the control of the KUKA youBot to test the remote operation with machine learning algorithms.