Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: Gesture-based interfaces, in which the user specifies commands 
        by simple freehand drawings, offer an alternative to traditional
        keyboard, menu, and direct manipulation interfaces.
        The ability to specify objects, an operation, and additional 
        parameters with a single intuitive gesture makes gesture-based 
        systems appealing to both novice and experienced users.
        
        Unfortunately, the difficulty in buildings gesture-based systems
        has prevented such systems from being adequately explored.
        This dissertation presents work that attempts to alleviate two
        of the major difficulties: the construction of gesture 
        classifiers and the integration of gestures into direct-
        manipulation interfaces.
        Three example gesture-based applications were built to 
        demonstrate this work.
        
        Gesture-based systems require classifiers to distinguish between
        the possible gestures a user may enter.
        In the past, classifiers have often been hand-coded for each new
        application, making them difficult to built, change, and 
        maintain.
        This dissertation applies elementary statistical pattern 
        recognition techniques to produce gesture classifiers that are 
        trained by example, greatly simplifying their creation and 
        maintenance.
        Both single-path gestures (drawn with a mouse or stylus) and
        multiple-path gestures (consisting of the simultaneous paths of
        multiple fingers) may be classified.
        On a 1 MIPS workstation, a 30-class single-path recognizer takes
        175 milliseconds to train (once the examples have been entered),
        and classification takes 9 milliseconds, typically achieving 97%
        accuracy.
        A method for classifying a gesture as soon as it is unambiguous
        is also presented.
        
        This dissertation also describes GRANDMA, a toolkit for 
        building gesture-based applications based on Smalltalk's Model/
        View/Controller paradigm.
        Using GRANDMA, one associates sets of gesture classes with 
        individual views or entire view classes.
        A gesture class can be specified at runtime by entering a few 
        examples of the class, typically 15.
        The semantics of a gesture class can be specified at runtime via
        a simple programming interface.
        Besides allowing for easy experimentation with gesture-based
        interfaces, GRANDMA sports a novel input architecture, capable 
        of supporting multiple input devices and multi-threaded 
        dialogues.
        The notion of virtual tools and semantic feedback are shown to 
        arise naturally from GRANDMA's approach.
Number: CMU-CS-91-202
Bibtype: TechReport
Month: dec
Author: Dean Harris Rubine
Title: The Automatic Recognition of Gestures
Year: 1991
Address: Pittsburgh, PA
Super: @CMUTR