Efficient strategies for finger movement classification using surface electromyogram signals - Prabhakar, Sunil Kumar; Won, Dong-Ok (Frontiers in Neuroscience, 2023)
One of the famous research areas in biomedical engineering and pattern recognition is finger movement classification. For the hand and finger gesture recognition, the most widely used signals are the surface electromyogram (sEMG) signals. With the help of sEMG signals, finger movement classification technique is presented using four proposed techniques in this work. The first technique proposed is a dynamic graph construction and graph entropy-based classification of sEMG signals. The second technique proposed encompasses the ideas of dimensionality reduction utilizing Local Tangent Space Alignment (LTSA) and Local Linear Co-ordination (LLC) with Evolutionary Algorithms (EA), Bayesian Belief Network (BBN), Extreme Learning Machine (ELM) and a hybrid model called EA-BBN-ELM is developed for the classification of sEMG signals. The third technique proposed utilizes the ideas of Differential Entropy (DE), High-Order Fuzzy Cognitive Maps (HFCM), Empirical Wavelet Transformation (EWT) and another hybrid model with DE-FCM-EWT and machine learning classifiers was developed for the classification of sEMG signals. The fourth technique proposed uses the ideas of Local Mean Decomposition (LMD) and Fuzzy C-Means Clustering along with a combined kernel Least Squares Support Vector Machine (LS-SVM) classifier. The best results are obtained for the DE-FCM-EWT hybrid model with SVM classifier reporting a classification accuracy of 98.21%. The second-best classification accuracy of 98.5% is obtained for the LMD-Fuzzy C-Means Clustering technique classified with a combined kernel LS-SVM model. The third best classification accuracy of 97.57% is obtained for LTSA based EA-BBN-ELM model.