An implementation of least square support vector machine (LS-SVM) for rehabilitation bio-signal analysis using surface electromyography (SEMG) signal

Nur Shidah, Ahmad Sharawardi (2014) An implementation of least square support vector machine (LS-SVM) for rehabilitation bio-signal analysis using surface electromyography (SEMG) signal. Project Report. UTeM. (Submitted)

Text AN IMPLEMENTATION OF LEAST SQUARE SUPPORT VECTOR MACHINE (LS-SVM) FOR REHABILITATION BIO-SIGNAL ANALYSIS USING SURFACE ELECTROMYOGRAPHY (SEMG) SIGNAL.pdf Download (448kB)

Abstract

This study are discussing about an implementation of LS-SVM for rehabilitation bio-signal analysis using surface slectromyogram (sEMG) signal. The sEMG has been widely used in clinical rehabilitation for its strong relationship with human muscle movement characteristics. The sEMG have been used in numerous studies for classification and have been successful implemented mostly on biofeedback system. But, the sEMG signal that obtains in the muscle is lost due to mixing with the high noise. Therefore, the goal of this study is to design the LS-SVM algorithm for muscle fatigue classification to enhance the accuracy and robustness in the classification process even though the present of high noise. The sEMG signal captured from the multifidus muscle and at flexor carpi radialis muscle is then will go through the features extraction process to obtain the root mean square (RMS), median frequency (MDF) and mean frequency (MF) features for better classification. The proposed LS-SVM that are been introduced by Suykens and Vandewalle in 1999 for classifies the muscle fatigue signal. Besides, many studies in support vector machine that was implement to classifies classes of different force intensity from the sEMG signal and validity are been carry out. The k-nearest neighbour (k-NN) and artificial neural network (ANN) will be the benchmark to the LS-SVM due to the widely use in the classification of bio-signal analysis. At the end of this experiment, the result shows that the accuracy and ROC value of LS-SVM have significant better than two other benchmarking technique and more robust.

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