HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
Gestures and widgets: performance in text editing on multi-touch capable mobile devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system, that can be integrated into an application to provide personalization capabilities. Experiments on an on-line gesture database were performed by considering various user personalization scenarios. The experiments show that the proposed evolving gesture recognition system continuously adapts and evolve according to new data of learned classes, and remains robust when introducing new unseen classes, at any moment during the lifelong learning process.