Abstract
The use of gesture recognition as a means of Human Computer Interaction for physically disabled users is discussed. The ability of motor-impaired computer users to make distinct, recognisable gestures is not exploited by current assistive technoloqies. A real-time computer vision system for recognition of one- and two-handed gestures defined by such users is described. An investigation into the feasibility of real-time, unencumbered recognition of gestures defined by motor-impaired users by means of Hidden Markov Models, trained with relatively few examples is performed and reported. Different feature vectors are compared and the trade-off between accuracy and training set size is explored: an important issue for such interactively trained systems.
Original language | English |
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Pages (from-to) | 197-203 |
Number of pages | 7 |
Journal | Technology and Disability |
Volume | 14 |
Issue number | 4 |
Publication status | Published - 2002 |