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Thesis made openly available per email from author, 5-1-2016.

by Song, Jingyan. / "January 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 110-118). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

This thesis is concerned with the development of virtual Human-Machine Interfaces (HMI) and navigation method for wheelchair systems. For virtual HMI, hand gesture recognition is employed, and two different hand gesture recognition algorithms have been developed. One is based on the geometric properties of a hand shape, and the other algorithm is based on the curvature of a hand shape contour. In the hand gesture recognition algorithm using geometric properties of a hand shape, eight non-dimensional parameters are computed and identifies hand shapes by comparing the ranges of the parameters to the statistical range information. This algorithm is invariant at scale, but does not work properly if the forearm of a hand shape is cluttered. The curvature based hand gesture recognition algorithm recognizes hand gestures using a combination of hand shape contour geometry and a non-dimensional quantity derived using the curvatures of the hand shape contour. The algorithm produces a set of signatures of the contour and identifies each hand gesture by finding matched template signatures. This algorithm is not affected by the forearm of a hand shape, but the scaling procedure is required. The developed gesture recognition system is implemented on a wheelchair in two different modes of operations, namely, the manual mode and the map (autonomous) mode. In the manual mode, the user continuously interacts with the wheelchair and controls the speed and the steering using the position and the orientation of hand gestures. In the map mode, the user selects a desired destination by pointing with a hand gesture onto a known map, and then the wheelchair initiates autonomous navigation. For wheelchair navigation, a doorway recognition algorithm and an obstacle avoidance algorithm have been developed. The wheelchair is localised by finding the doorway template in the specified zone. If the doorway recognition algorithm does not detect the doorway, it navigates to find the doorway using the obstacle avoidance algorithm. The obstacle avoidance algorithm finds obstacle edge points using range data and decides a safe passage for wheelchair navigation to find the doorway. Results obtained by implementing the above mentioned algorithms are presented.

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