Protractor: a fast and accurate gesture recognizer

Yang Li. 2010. Protractor: a fast and accurate gesture recognizer. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, 2169-2172. DOI=10.1145/1753326.1753654 http://doi.acm.org/10.1145/1753326.1753654


This paper discusses Protractor, a template-based gesture recognizer focused on low memory requirements and fast classification. Protractor uses a nearest neighbor approach, learning from user input training data. The gesture can be specified to be sensitive to orientation, or invariant. The preprocessing of the gesture is similar to the $1 recognizer, with 16 points total used. Classification is accomplished using the optimal angular distances (inverse cosine distance between template and sample vector values). Protractor also uses a closed form solution to find a rotation of the gesture which leads to the least distance. Protractor was compared to the $1 recognizer and was found to perform similarly, but with slightly faster response times.

Protractor is a promising contribution for mobile devices constrained for processing power and memory. It offers a simple algorithm for user defined gestures.