Support Vector Machines for detecting recovery from knee replacement surgery using quantitative gait measures

Knee osteoarthritis (OA) is one of the leading causes of disability among the elderly which, depending on severity, may require surgical intervention. Knee replacement surgery provides pain relief and improves physical function including gait. Gait dysfunction such as altered spatio-temporal measures and gait asymmetry both pre- and post-surgery, however, may still persist after the surgery. In this paper, we investigated the application of support vector machines (SVM) to classify gait patterns pertaining to knee OA before surgery based on spatio-temporal gait parameters and to investigate whether SVM can assess gait improvement at 2 months following knee replacement surgery. Test results indicate that the SVM can identify the OA gait from the healthy ones with a max leave one out (LOO) accuracy of 94.2%. When feature selection technique was applied, the accuracy improved to 97.1% using only 2 symmetry index features. Further, the post surgery test results by the SVM indicated 4 patients still had altered gait. This suggests that subject gait symmetry should be monitored closely after surgery to assess treatment outcomes and recovery.