Computational and Mathematical Methods in Medicine
Volume 2011 (2011), Article ID 569898, 14 pages
doi:10.1155/2011/569898
Research Article

Arthritic Hand-Finger Movement Similarity Measurements: Tolerance Near Set Approach

Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

Received 12 August 2010; Accepted 9 February 2011

Academic Editor: Reinoud Maex

Copyright © 2011 Christopher Henry and James F. Peters. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a tolerance space view of digital images and the introduction of image resemblance measures. The motivation for this work is both to quantify and to visualize differences between hand-finger movements in an effort to provide clinicians and physicians with indications of the efficacy of the prescribed rehabilitation exercise. The more recent introduction of tolerance near sets has led to a useful approach for measuring the similarity of sets of objects and their application to the problem of classifying image sequences extracted from videos showing finger-hand movement during rehabilitation exercise. The approach to measuring the resemblance between hand movement images introduced in this paper is based on an application of the well-known Hausdorff distance measure and a tolerance nearness measure. The contribution of this paper is an approach to measuring as well as visualizing the degree of separation between images in arthritic and nonarthritic hand-finger motion videos captured during rehabilitation exercise.