Article Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot
In this paper, we use data from the Microsoft Kinect sensor that processes the captured image
of a person using and extracting the joints information on every frame. Then, we propose the creation of
an image derived from all the sequential frames of a gesture the movement, which facilitates training in a
convolutional neural network. We trained a CNN using two strategies: combined training and individual
training. The strategies were experimented in the convolutional neural network (CNN) using the
MSRC-12 dataset, obtaining an accuracy rate of 86.67% in combined training and 90.78% of accuracy
rate in the individual training.. Then, the trained neural network was used to classify data obtained from
Kinect with a person, obtaining an accuracy rate of 72.08% in combined training and 81.25% in
individualized training. Finally, we use the system to send commands to a mobile robot in order to control
Copyright (c) 2019 Iberamia & The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Open Access publishing.
Lic. under Creative Commons CC-BY-NC
Inteligencia Artificial (Ed. IBERAMIA)
ISSN: 1988-3064 (on line).
(C) IBERAMIA & The Authors