Digital Archives Initiative
Memorial University - Electronic Theses and Dissertations 2
menu off  add document to favorites : add page to favorites : reference url back to results : previous : next
 Search this object:
 0 hit(s) :: previous hit : next hit
  previous page : next page
Document Description
TitleGesture recognition as a means of human-machine interface
AuthorHale, Rodney D., 1969-
DescriptionThesis (M.Eng.)--Memorial University of Newfoundland, 1998. Engineering and Applied Science
Paginationix, 185 leaves : ill.
SubjectUser interfaces (Computer systems); Pattern recognition systems
Degree GrantorMemorial University of Newfoundland. Faculty of Engineering and Applied Science
DisciplineEngineering and Applied Science
NotesBibliography: leaves 95-97.
AbstractThe 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.
Resource TypeElectronic thesis or dissertation
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifiera1320363
RightsThe 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.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(17.85 MB) --
CONTENTdm file name199992.cpd