Neural Network Classifier for Hand Motion Detection from EMG Signal

EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. Additionally, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This paper represents the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The conventional and most effective time and time-frequency based feature set is utilized for the training of neural network. The obtained results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%. Furthermore, when the trained network tested on unknown data set, it successfully identify the movement types.

Authors and Affiliations

1. Department of Electrical & Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Kuala Lumpur

   Md. R. Ahsan, M. I. Ibrahimy & O. O. Khalifa

Editors and Affiliations

1. Department of Biomedical Engineering Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

   Noor Azuan Abu Osman , Wan Abu Bakar Wan Abas , Ahmad Khairi Abdul Wahab  & Hua-Nong Ting , ,  & 

Reprints and permissions

© 2011 Springer-Verlag Berlin Heidelberg

Policies and ethics