Specifying Gestures by Example

Bibliographical Information:
Rubine. "Specifying Gestures by Example." SIGGRAPH '91 Proceedings of the 18th annual conference on Computer graphics and interactive techniques. Pages 329-337. ACM New York, NY, USA.

URL:
http://dl.acm.org/citation.cfm?id=122753

This paper focuses on the discussion of GRANDMA (Gesture Recognizers Automated in a Novel Direct Manipulation Architecture), which is a toolkit for rapidly adding gesture to an interface designed to recognize sketches. At the time of the paper's publication, most sketch templates of gestures to be recognized required careful hand coding and maintenance as more gestures were added. This tool, however, provides a system for the owner of the gesture curator to continue adding new gestures via example. By implementing gesture recognition on a "curator" level, gestures themselves are saved as templates that can then be used identify input from users. This algorithm is considered lightweight enough for implementation as part of the back-end of some larger sketching system. GRANDMA uses a set of 13 features, which analyzes the input of the new template sketches to create classifiers on its own when creating these templates. Some of these include cosine and sine are included to determine the stroke's angle, bounding box, length, duration of the gesture, "sharpness" of the gesture (to help differentiate between, for example, the letters "U" and "V"), among others. These help generate attributes of these sketches that will then be associated as features of said templates.

This paper presents valuable insight into the on-the-fly generation of templates and the associated features. I find the inclusion and simple explanation of each of the mathematical formulas used to identify the features was useful in explaining the simplicity of the system. Indeed, one of the bigger features of the system is the fact that it is lightweight. However, the system was not implemented in a case study, nor was it shown to work in an end application as it was designed to be. It also spoke about some design decisions such as whether to recognize or not recognize ambiguous user input that is likely to be mislabeled, but since the application was not shown to have a "field test" it is unclear if these decisions were useful or based on user data.