TY  - JOUR
T1  - Convolutional Neural Training for Robotic Control Through Hand Gestures
AU - Jimenez, M. Robinson AU - Paola Nino, S. AU - F. Aviles, Oscar AU - Ovalle, Diana 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 21
SP  - 8949
EP  - 8954
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.8949.8954
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.8949.8954
KW  - Deep learning
KW  -convolutional network
KW  -robotic control
KW  -hand gestures
KW  -human computer interaction
KW  -convolutional
KW  -perc
AB  - This study presents the training of a convolutional neural network to identify different control signals
made by hand, that allow to command a robotic mobile. Initially a database of 4000 images is established
regarding the different control signals for the manipulation of the mobile, corresponding to 10 different users
and after this the base structure of the convolutional neural network and the results of its training are
determined. The robotic control algorithm was validated by means of navigation tests performed by 5 different
users to those employed in the training stage where a percentage of accuracy was obtained to perform linear
paths on average of 93.2% and for non-linear paths of 79%. Training algorithms for convolutional neural
networks have not been evaluated in robotic navigation control tasks for transporting objects.
ER  -