MAGIC 2.0: A web tool for false positive prediction and prevention for gesture recognition systems

TitleMAGIC 2.0: A web tool for false positive prediction and prevention for gesture recognition systems
Publication TypeConference Paper
Year of Publication2011
AuthorsKohlsdorf, D, Starner, TE, Ashbrook, DL
Conference NameAutomatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on
Date Publishedmarch
Keywordsandroid mobile phone, everyday gesture library database, false positive prediction and prevention, gesture recognition, hidden Markov model, hidden Markov models, indexable symbolic aggregate approximation, interactive searching, interface design process, iSAX, large database, MAGIC 2.0, mobile handsets, nearest neighbor, sensors, very large databases, Web service, Web services, Web tool
Abstract

False positives are a common problem for interfaces that rely on gesture recognition. Often a gesture can seem fine in development but is found to trigger accidentally during an initial deployment of the interface, restarting development and increasing expense. In this work we introduce MAGIC 2.0, a technique for false positive prediction and prevention that can be used interactively during the interface design process. To ground our research, we implement MAGIC 2.0 as a web service and develop gesture interfaces using sensors on common Android mobile phone platforms. We use iSAX (indexable Symbolic Aggregate approXimation) to enable interactive searching ( lt;;2 sec/example) of a large database ( gt;;1,500,000 sec) of everyday user movements on a standard workstation to determine if a candidate gesture will trigger accidentally during use of an interface. We perform a user-independent study that suggests that the number of matches to this Everyday Gesture Library (EGL) database is indeed predictive of a candidate gesture's suitability. We compare iSAX to hidden Markov models (HMMs) and nearest neighbor with respect to accuracy and speed for the EGL search. Using iSAX on the EGL, we also develop a #x201C;garbage #x201D; class and show that including this class in recognition reduces errors.

DOI10.1109/FG.2011.5771412