TY  - JOUR
T1  - Hand Gesture Recognition Using Electromyographic Signals Throw a
Deep Convolutional Neural Network
AU - Pinzon Arenas, Javier O. AU - Jimenez Moreno, Robinson AU - Hernandez Beleno, Ruben D. 
JO  - Research Journal of Applied Sciences
VL  - 13
IS  - 9
SP  - 482
EP  - 490
PY  - 2018
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2018.482.490
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2018.482.490
KW  - Deep convolutional neural network
KW  -power spectral density
KW  -electromyographic signal
KW  -hand
gesture recognition
KW  -Myo armband
AB  - This study presents the implementation of a convolutional neural network focused on the recognition
of hand gestures for this case 3 specific types of gestures using the EMG signals as input which were acquired
through the Myo armband device and processed by means of a characteristic map extraction technique which
is the power spectral density. The development of this work is divided into 2 phases where the first consists
of the acquisition and processing of the electromyographic signals of different users with different arm
thickness from which 2 databases were built and the second phase describes the architecture of the
convolutional neural network to be used and the training that was performed with each database independently,
obtaining two trained networks. Finally, two types of tests are performed, a validation test in which the
accuracy of the two trained networks is verified where a accuracy rate of 91.7 and 92.5% was achieved and a
real-time behavioral test where the two networks responded adequately, meaning that the use of convolutional
neural networks for the recognition of hand gestures by means of electromyographic signals can reach high
ranges of accuracy, even greater than 90%.
ER  -