Incremental Learning of Hand Gestures Based on Submovement Sharing

This paper presents an incremental learning method for hand gesture recognition that learns the individual movements in each gesture of a user. To recognize the movement, we use a subunit-based dynamic time warping method, which treats a hand movement as a sequence of ubmovements. In our method, each hand movement is decomposed into submovements and the arrangement of submovements is reflected in the training sample database. Experimental results from the lassification of ten gestures demonstrate that our method can improve the recognition rate compared with a method without incremental learning. In addition, the experimental results show that incremental learning of a single class of gestures can improve the recognition rate of multi-class gestures using our method.