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.

翻译：本文提出了一种新颖的动态手势识别方法，旨在促进人机之间的无缝交互。该方法为每个机器人操作任务分配特定的手势指令。由于可能存在多个任务，因此需要定义多个手势。这些手势在实际使用中可能产生多种动态变化。基于高斯混合模型的无监督模型能够实时准确地识别机器人接收到的不同手势的所有动态变化。训练阶段与实时测试阶段的准确率结果验证了该方法的有效性。