Forearm movements classification of EMG signals using Hilbert Huang transform and artificial neural networks

Abstract

Vast information regarding muscle activity for clinical and engineering applications can be obtained via the EMG (Electromyogram). EMG signal are acquired through surface electrodes which are placed on target muscle set of healthy subjects aged between 23 to 30 years. In this work, six forearm movements have been chosen for classification purpose for both left and right hand. With Hilbert Huang Transform method a total of 21 features of time-frequency domain are extracted for 10 healthy subjects and classified using conjugate gradient method of supervised learning technique using artificial neural networks (ANN). The average accuracy at IMF-1 level obtained is 85.8% for left hand movements, and 86.2% for right hand movement classification. The results of using the Hilbert Huang Transform based ANN classification are quite promising when compared to another classification techniques as K-NN, QDA, LDA [21] and Mahdi Khezri et al. different signal acquisition and classification techniques[26]. The technique can be used for practical implementation of prosthesis for movement classification.