Acquisition of Embodied Knowledge on Gesture Motion by Singular Value Decomposition
Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human embodied knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring embodied knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.