NDLI: Automatic Recognition of Gait Patterns Exhibiting Patellofemoral Pain Syndrome Using a Support Vector Machine Approach

Content Provider	IEEE Xplore Digital Library
Author	Lai, D.T.H. Levinger, P. Begg, R.K. Gilleard, W.L. Palaniswami, M.
Copyright Year	1997
Abstract	Patellofemoral pain syndrome (PFPS) is a common disorder that afflicts people across all age groups, and results in various degrees of knee pain. The diagnosis of PFPS is difficult since the exact biomechanical factors and the extent to which they are affected by the disorder are still unknown. Recent research has reported significant statistical differences in ground reaction forces (GRFs) and foot kinematics, which could be indicative of PFPS, but the interrelationship between many of these measures and the pathology have been absent so far. In this paper, we applied the support vector machines (SVMs) to detect PFPS gait based on 14 GRF and 16 foot kinematic features recorded from 27 subjects (14 healthy and 13 with PFPS). The influence of combined gait parameters on classification performance was investigated through the use of a feature-selection algorithm. The optimal feature set was then compared against the most statistically significant individual features (p < 0.05) found by previous study. Test results indicated that GRF features alone resulted in a higher leave-one-out (LOO) classification accuracy (85.15%) compared to 74.07% using only kinematic features. A hill-climbing feature-selection algorithm was applied to determine the subset of combined kinematic and kinetic features, which provided optimal classifier performance. This subset, which consists of six features (two from GRF and four from foot kinematic features), provided an improved LOO accuracy of 88.89% . The optimal feature set detected by the SVM, which best identified gait characteristics of PFPS, was found to be closely related to inferential statistical analysis with the added distinction that the SVM could potentially be deployed as an automated system for detecting gait changes in patients with PFPS.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Computer Society
Page Count	8
File Size	546092
Starting Page	810
Ending Page	817
File Format	PDF
ISSN	10897771
Volume Number	13
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern recognition Pain Support vector machines Kinematics Foot Support vector machine classification Knee Force measurement Pathology Testing support vector machines (SVMs) Gait analysis ground reaction forces (GRFs) kinematics patellofemoral pain syndrome (PFPS)
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Biotechnology

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