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Proceedings Paper

Object and event recognition for stroke rehabilitation
Author(s): Ahmed Ghali; Andrew S. Cunningham; Tony P. Pridmore
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Paper Abstract

Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient’s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient’s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

Paper Details

Date Published: 23 June 2003
PDF: 10 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.503470
Show Author Affiliations
Ahmed Ghali, Univ. of Nottingham (United Kingdom)
Andrew S. Cunningham, Univ. of Nottingham (United Kingdom)
Tony P. Pridmore, Univ. of Nottingham (United Kingdom)


Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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