Visual Similarity of Pen Gestures

Visual Similarity of Pen Gestures

A. Chris Long, Jr., James A. Landay, Lawrence A. Rowe, and Joseph Michiels

Department of Electrical Engineering and Computer Science

University of California at Berkeley

Berkeley, CA 94720-1776

{allanl, landay, rowe, cujoe} @cs.berkeley.edu

+1-510-643-7106

http://www.cs.berkeley.edu/~{allanl, landay, rowe}

Howdy! 

             In this post, I'm going to briefly summarize the above research paper and express my opinion on it. 

             In the paper, the authors talk about their research on pen gestures and their perceived similarity among other things. Pen gestures are getting more and more popular everyday as they are easier to work with and remember as compared to text commands. But a lot of times, users of a gesture- based input system might confuse gestures or the system might recognize two different gestures as the same one. The authors designed two experiments to figure out the underlying features that make a gesture uniquely perceivable by the user. 

            For the first experiment, they created gestures that varied a lot from each other in terms of how an user might perceive them. 

             The users were shown all possible triads (groups of 3 gestures at a time) from the above gesture set and asked to mark the most different gesture in each triad. The authors analyzed the data collected and made a list of 22 features that they thought might be distinguishing factors for a gesture. 

             One of the goals of the second experiment was to figure out how different features affect the perceived similarity of gestures. The authors made three different gesture sets. The first gesture set was designed to explore absolute angle and aspect, the second to explore length and area and the third to explore rotation. The authors picked two gestures from each set and added them to a fourth set to make the number of triads smaller. The procedure was similar to the first experiment. 

            The model learned in each experiment was used to predict similarities between pairs of gestures from the gesture set used in the other experiment. The results showed that the model obtained from the first experiment was a little bit better than the model obtained from the second experiment. The results from using the first model agreed with the users' input with a correlation of 0.56. They also obtained some interesting results like neither the length nor the area of the bounding box is a very strong distinguishing feature while the logarithm of aspect is a strong influence on the similarity of gestures.

I found the paper very interesting. Such analysis can be very helpful in designing gestures that are easy to remember and are convenient. It can help a great deal in enhancing the user experience of a pen- based interface. 

I used the following sources for this blog post:

[1] Proceeding CHI '00 Proceedings of the SIGCHI conference on Human Factors in Computing Systems. Pages 360-367. ACM New York, NY, USA ©2000

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