Abstract
This paper presents a new knee abnormality diagnosis system using surface EMG signals. The time-frequency domain is obtained for EMG raw data using Short Time Fourier Transform (STFT) method, and filtered with the local range texture analysis to produce the final 2D extracted feature. For system evaluation, public SEMG database is chosen, and experiments show that EMG data of Vastus Medialis (VM) muscle with Convolutional Neural Network (CNN) classifier provide the highest accuracy of 91%.
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Issa, S., Khaled, A.R. (2022). Knee Abnormality Diagnosis Based on Electromyography Signals. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_13
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DOI: https://doi.org/10.1007/978-3-030-96302-6_13
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