Dynamic Hand Gesture Recognition for Robot Manipulator Tasks

TL;DR

This paper introduces an unsupervised Gaussian Mixture model for real-time recognition of dynamic hand gestures, enabling improved human-robot interaction by accurately identifying multiple gesture variations during robot tasks.

Contribution

It presents a novel unsupervised approach for real-time dynamic gesture recognition tailored for robot manipulators, handling gesture variations effectively.

Findings

High accuracy in training and testing phases

Effective real-time recognition of gesture variations

Enhanced human-robot interaction capabilities

Abstract

This paper proposes a novel approach to recognizing dynamic hand gestures facilitating seamless interaction between humans and robots. Here, each robot manipulator task is assigned a specific gesture. There may be several such tasks, hence, several gestures. These gestures may be prone to several dynamic variations. All such variations for different gestures shown to the robot are accurately recognized in real-time using the proposed unsupervised model based on the Gaussian Mixture model. The accuracy during training and real-time testing prove the efficacy of this methodology.