Diagnosis of Knee Osteoarthritis Based on Kalman Filter

In this paper, a noninvasive method for Knee Osteoarthritis (KOA) detection and diagnosis is proposed using data from surface electromyogram (sEMG) signals with the purpose of accessing the state of KOA in the early stage. In our experiment, sEMG are collected from rectus femoris, vastus medialis, biceps femoris, semitendinosus muscle of control group and KOA group respectively when they are in the walking model, then parameters of autoregressive recurrent model (ARM) based on which are extracted by the well-known Kalman filter as the characteristic vectors, which is used to train the RBF neural network. Finally, the knee osteoarthritis will then be diagnosed through the RBF neural network, It is shown that a much improved result over the traditional method is achieved over classifiers based on RBF neural network.