Design and performance analysis of artificial neural network for hand motion detection from EMG signals - Ibrahimy, Muhammad Ibn; Ahsan, Md. Rezwanul; Khalifa, Othman Omran (IDOSI Publication, 2013)
Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This article represents the classification of Electromygraphy (EMG) signal for the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). The neural network is of backpropagation type, trained by Levenberg-Marquardt training algorithm. Before the classification process, the EMG signals have been pre-processed for extracting some features from them. The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized feature set with supervised learning method. The obtained results show that the designed network can successfully classify the hand motions from the EMG signals with the success rate of 88.4%. The performance of the designed network has also been compared to similar research work, whereby it certainly shows the outperformance.