The ACM International Conference on Multimodal Interaction, was recently held in Istanbul, Turkey. My co-author Radu-Daniel Vatavu presented the poster for our paper entitled “Gesture Heatmaps: Understanding Gesture Performance with Colorful Visualizations,” which you can check out here. I was sorry not to be able to attend this year, but perhaps next year (it will be in Seattle, WA).
Tag Archives: user consistency
My colleagues, Radu-Daniel Vatavu and Jacob O. Wobbrock, and I have had another paper accepted for publication! This paper continues our efforts to understand patterns and inconsistencies in how people make touchscreen gestures. This time, we introduced a way to use heatmap-style visualizations to examine articulation patterns in gesture datasets, and our paper “Gesture Heatmaps: Understanding Gesture Performance with Colorful Visualizations” was accepted to the ACM International Conference on Multimodal Interaction, to be held in Istanbul, Turkey, in November 2014. Here is the abstract:
We introduce gesture heatmaps, a novel gesture analysis technique that employs color maps to visualize the variation of local features along the gesture path. Beyond current gesture analysis practices that characterize gesture articulations with single-value descriptors, e.g., size, path length, or speed, gesture heatmaps are able to show with colorful visualizations how the value of any such descriptors vary along the gesture path. We evaluate gesture heatmaps on three public datasets comprising 15,840 gesture samples of 70 gesture types from 45 participants, on which we demonstrate heatmaps’ capabilities to (1) explain causes for recognition errors, (2) characterize users’ gesture articulation patterns under various conditions, e.g., finger versus pen gestures, and (3) help understand users’ subjective perceptions of gesture commands, such as why some gestures are perceived easier to execute than others. We also introduce chromatic confusion matrices that employ gesture heatmaps to extend the expressiveness of standard confusion matrices to better understand gesture classification performance. We believe that gesture heatmaps will prove useful to researchers and practitioners doing gesture analysis, and consequently, they will inform the design of better gesture sets and development of more accurate recognizers.
Check out the camera ready version of our paper here. Our paper will be presented as a poster at the conference, and I’ll post the PDF when available.
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.