Dynamic Hand Gesture Recognition for Robot Manipulator Tasks
This addresses human-robot interaction for manipulator tasks, but it appears incremental as it applies an existing method to a specific domain.
The paper tackles the problem of recognizing dynamic hand gestures for robot manipulator tasks by proposing an unsupervised Gaussian Mixture model, achieving accurate real-time recognition of gesture variations.
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.