Detection of knee abnormality from surface EMG signals by artificial neural networks

Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was determined. The highest classification performance of proposed model was obtained in rate fiction 80%–20% and 70%–30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals.