Understanding Gestures Project: Collecting and analyzing gestures from children

The $-family of recognizers are lightweight, easy to implement gesture recognizers that allow for quick development of 2-D gesture-based interfaces. These algorithms are short (less than 100 lines of code each) allowing for easy incorporation by developers into new projects. These algorithms currently achieve 98-99% accuracy for recognizing gestures made by adults, but only about 84% accuracy for gestures from kids. Thus, we are working on extending these algorithms so that they can achieve better recognition for children’s gestures. We are currently working on a study to gather a set of gesture data from kids as part of our MTAGIC project. After we collect this data, we will study it and attempt to find ways to improve the algorithms based on our findings. For more information on previous work on the $-family of recognizers, see the below links:
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I am a first year Ph.D. student at the University of Florida studying Computer Engineering. I recently received my B. S. in Computer Science from Auburn University. Working with the $-family of recognizers has given me an excellent introduction to the field of gesture recognition. I have also been able to study the experiments used to verify these gesture recognition algorithms, which has helped me learn about research methods in Human-Centered Computing.

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