$-Family of Recognizers Project Update

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.