Controlling Robot Car Movement Using Hand Gesture Recognition

Abstract

This paper describes the implementation of
movement control for robotic car using hand gesture
recognition which uses deep learning algorithm.
Therefore, proposed technique is hassle free as control
is not based on joysticks or switches. There are six
conditions considered for robot car movement control
as ‘Backword’, ‘Forward’, ‘Left’, ‘No-Motion’, ‘Right’
and ‘Stop’ using different hand gestures. There are
many researchers worked on this area using different
sensors, machine learning algorithms and deep
learning algorithms. Limitations of the state of art
techniques are studied in this paper and designed a new
modified convolutional neural network (CNN) for
gesture recognition which controls movement of robot
car. Dataset is created which generates 1000 Gray scale
images for each type of gesture. Training modified
CNN model gives prediction accuracy of 98.4024 %
while random forest machine learning classifier gives
prediction accuracy of 69%. It is observed that proposed
model gives better accuracy compared to state of art
technique for controlling movement of robot car using
hand gesture. Obtained hand gesture class can be send
to robot using Arduino controller for controlling
movement.