The $-family of recognizers isn’t just about how to build better recognition algorithms, it’s also about understanding patterns and inconsistencies in how people make gestures, too. This kind of knowledge will help inform gesture interaction both in terms of developing better recognizers, and designing appropriate gesture sets. In this vein, I have had a paper accepted, along with my collaborators, Jacob O. Wobbrock and Radu-Daniel Vatavu, to the Graphics Interface 2013 conference, on characterizing patterns in people’s execution of surface gestures from existing datasets. The paper is titled “Understanding the Consistency of Users’ Pen and Finger Stroke Gesture Articulation,” and here is the abstract:
Little work has been done on understanding the articulation patterns of users’ touch and surface gestures, despite the importance of such knowledge to inform the design of gesture recognizers and gesture sets for different applications. We report a methodology to analyze user consistency in gesture production, both between-users and within-user, by employing articulation features such as stroke type, stroke direction, and stroke ordering, and by measuring variations in execution with geometric and kinematic gesture descriptors. We report results on four gesture datasets (40,305 samples of 63 gesture types by 113 users). We find a high degree of consistency within-users (.91), lower consistency between-users (.55), higher consistency for certain gestures (e.g., less geometrically complex shapes are more consistent than complex ones), and a loglinear relationship between number of strokes and consistency. We highlight implications of our results to help designers create better surface gesture interfaces informed by user behavior.