Combining Pattern Classifiers for Gait Analysis
A Clinical Decision Support System for Grading Knee Osteoarthritis
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Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. It serves not only as a measure of treatment outcome, but also as a useful tool in planning ongoing care of various neuromuskuloskeletal disorders such as CP, OA, as a support to other approaches such as X-rays, chemical tests. Gait process is realized in a laboratory by the use of markers placed on specified parts of the body and computer-interfaced cameras to track the walking motion and force platforms embedded in the walkway. Interpretation of the resultant high dimension...
Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. It serves not only as a measure of treatment outcome, but also as a useful tool in planning ongoing care of various neuromuskuloskeletal disorders such as CP, OA, as a support to other approaches such as X-rays, chemical tests. Gait process is realized in a laboratory by the use of markers placed on specified parts of the body and computer-interfaced cameras to track the walking motion and force platforms embedded in the walkway. Interpretation of the resultant high dimensional and huge amount of data requires particular expertise. The aim of pattern recognition research for clinical gait analysis is to find ways to assist decision making and treatment planning. This book presents a clinical decision support system for detecting and scoring of a knee disorder. It emphasizes new approaches like combining classifiers which produced promising results (up to 94%) for discriminating normal and sick subjects. This book should be especially useful to gait analysis professionals and pattern recognition researchers dealing with diverse and high dimensional clinical data.
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