An implementation of FCM-RBF technique for elbow joint flexion using single channel surface electromyography (SEMG) signals

Soh Jia, Shan (2014) An implementation of FCM-RBF technique for elbow joint flexion using single channel surface electromyography (SEMG) signals. Project Report. UTeM. (Submitted)

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Abstract

Muscle force to every joint position movement is very important. Human movement involves the activation and control of muscle force. Therefore, EMG evaluation had been recorded from movement activity produced by skeletal muscles to predict the muscle force from human motion. This study mainly aim to predict the muscle force during elbow joint flexion using single channel surface Electromyography (sEMG) signals. However, the challenge of using the single channel sEMG signals was it facing the problem of accuracy in prediction although it is cheaper than using multi-channel signals. Therefore, a good prediction method had been carried out using the technique of Radial Basis Function (RBF) Network hybrid with teclmique of Fuzzy C-means (FCM). Most of the researched were used RBF and Multilayer Perceptron (MLP) technique only to predict the muscle force. Anyway, the technique of RBF in prediction is better than MLP. To improve the performance of RBF, FCM was used to find the cluster centres and used in hidden layer of RBF network so that it can improve the performance during the prediction of muscle force. The result shown that the FCM did the clustering according to the pre­ set category, so the hidden node in the hidden layer had been cluster in the different node and it affect the high Root Mean Square Error (RMSE) values produced. MLP using nntoolbox also had been carried out in this research to compare the RMSE results with the technique of RBF based on FCM. The result had proof that the technique of RBF based on FCM is better than MLP technique.

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