Assessment of cartilage disorder in knee with VAG signals using wavelet transform and neural network

Detection of knee joint pathology at an early stage is vital for the joint affected by degenerative disorders, since appropriate treatment can facilitate the extended functionality at the knee joint. Vibroarthrographic (VAG) signals could be used to develop a non-invasive methodology for monitoring articular cartilage pathology. This paper deals with the development of such a tool for the detection of abnormalities in knee joint using wavelet transform. Since VAG signals are non - stationary, wavelet transform are best suited to extract the statistical feature. The classification is performed using feed forward back propagation neural network.