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 backpropagation
(BP) network with Levenberg-Marquardt training
algorithm has been utilized for the classification of EMG
signals. The conventional and most effective time and timefrequency
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
