Memorial University DAI: Gesture recognition as a means of human-machine interface

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Document Description

Title	Gesture recognition as a means of human-machine interface
Author	Hale, Rodney D., 1969-
Description	Thesis (M.Eng.)--Memorial University of Newfoundland, 1998. Engineering and Applied Science
Date	1998
Pagination	ix, 185 leaves : ill.
Subject	User interfaces (Computer systems); Pattern recognition systems
Degree	M.Eng.
Degree Grantor	Memorial University of Newfoundland. Faculty of Engineering and Applied Science
Discipline	Engineering and Applied Science
Language	Eng
Notes	Bibliography: leaves 95-97.
Abstract	The development of a reliable multi-modal human-machine interface has many potential applications. The interface with a personal computer has become very common yet many disabled users have limited access due to the restrictiveness of the current interface. An improved interface would improve the quality of life for disabled users and has applications in controlling machinery in an industrial setting. Many different types of gestures ranging from head gestures, headpointing, hand and arm gestures are being investigated. A wide variety of classification techniques are available. These techniques range from simple clustering routines to complex adaptive routines. This work compares the recognition results of four pattern recognition techniques, the k-nearest neighbor, a Mahalanobis distance classifier, a rule based classifier and hidden Markov models. The techniques were tested on a set of six hand gestures captured using The Flock of Birds data collection system. The best average recognition result was 97% obtained from the k-nearest neighbor classifier, the Mahalanobis distance classifier had an average recognition rate at 92%, the rule based classifier had an average recognition rate at 89% and the hidden Markov models had the lowest average recognition results at 83%. The hidden Markov models are the most complex of the four techniques studied. Although the average recognition results were lower, they are rich in mathematical structure and can be used to model very complex observations.
Type	Text
Resource Type	Electronic thesis or dissertation
Format	Image/jpeg; Application/pdf
Source	Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifier	a1320363
Rights	The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
Collection	Electronic Theses and Dissertations
Scanning Status	Completed
PDF File	(17.85 MB) -- http://collections.mun.ca/PDFs/theses/Hale_RodneyD.pdf
CONTENTdm file name	199992.cpd