Enhancing Visualization with Expressive Motion

Abstract

Motion is a fundamental perceptual channel, and we derive substantial information about the world around us from how things move in that environment, informing both cognitive and affective interpretation. While previous information visualization research has explored how motion can represent data, support pattern-matching and ease transitions [2, 13, 15], how to represent the rich expressive and affective capacity of animation remains a challenge. A history of expressive movement in the performative and visual arts offers insight into how movement patterns may carry meaning, yet current research provides only a limited understanding of affective motion features and the potential for expressive movements to be abstracted and applied in other contexts. Descriptive frameworks of human movement such as Laban Movement Analysis (LMA) provide rich analytical tools and principles that model the expressive capacity of movement but these remain descriptive rather than computationally tractable. In this paper we present an enhanced computational model for expressive motion that we developed with movement experts. We briefly describe the results of an ongoing study with choreographers and performers in using these features to express movement qualities and discuss potential applications for information visualization.