By Topic

A Neural Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Deyou Xu ; Artillery Academy at Nanjing, 211132,China

The recognition of hand gestures is a challenging task for the high degrees of freedom of hand motion. We develop a virtual reality based driving training system of self-propelled gun (SPG). For this system, a dataglove with 18 sensors is employed to perform some driving tasks such as pressing switches, manipulating steering wheel, changing gears, etc. To accomplish these tasks, some hand gestures must be defined from the dataglove sensors data. A feedforward neural network can represent an arbitrary functional mapping so it is possible to map raw data directly to the required hand gestures. This paper uses BP neural network to recognize the hand patterns which exist in the raw sensor data of the dataglove. A pattern set of 300 hand gestures is used to train and test the neural network. The recognition system achieves good performance. It can be effectively used in our virtual reality training system of SPG to perform various manipulating tasks in a more fast, precise, and natural way

Published in:

18th International Conference on Pattern Recognition (ICPR'06)  (Volume:3 )

Date of Conference:

0-0 0