User Interaction Optimization for an Evolving Classifier of Handwritten Gesture Commands

Abstract

Touch sensitive interface enables new interaction methods, like using gesture commands. The use of gesture commands give rise to a cross-learning situation where the user has to learn and memorize the command gestures and the classifier has to learn and recognize drawn gestures. To easily memorize more than a dozen of gesture commands, it is important to be able to customize them. The classification task associated with the use of customized gesture commands is complex because the classifier only has very few samples per class to start learning from. We thus need an evolving recognition system that can start from very few data samples and that will learn incrementally to achieve good performance after some using time. This article presents the impact of using rejection based user interactions to supervise the on-line training of the evolving classifier. The objective is to obtain a gesture command system that cooperates as best as possible with the user: to learn from its mistakes without soliciting him too often. To detect confusing classes we apply confusion reject principles to our evolving recognizer, which is based on a first order fuzzy inference system. A significant user experiment has been performed on 63 persons that validates our approach. This user experiment shows the interest of optimizing user interactions by taking into account the confusion detection capability of our recognition system.