GestureAnalyzer: visual analytics for pattern analysis of mid-air hand gestures

Abstract

Understanding the intent behind human gestures is a critical problem in the design of gestural interactions. A common method to observe and understand how users express gestures is to use elicitation studies. However, these studies require time-consuming analysis of user data to identify gesture patterns. Also, the analysis by humans cannot describe gestures in as detail as in data-based representations of motion features. In this paper, we present GestureAnalyzer, a system that supports exploratory analysis of gesture patterns by applying interactive clustering and visualization techniques to motion tracking data. GestureAnalyzer enables rapid categorization of similar gestures, and visual investigation of various geometric and kinematic properties of user gestures. We describe the system components, and then demonstrate its utility through a case study on mid-air hand gestures obtained from elicitation studies.