Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network
Abstract
Purpose
Main purpose is to present methodology which allows efficient hand gesture recognition using low-budget, 5-sensor data glove. To allow widespread use of low-budget data gloves in engineering virtual reality (VR) applications, gesture dictionaries must be enhanced with more ergonomic and symbolically meaningful hand gestures, while providing high gesture recognition rates when used by different seen and unseen users.
Design/methodology/approach
The simple boundary-value gesture recognition methodology was replaced by a probabilistic neural network (PNN)-based gesture recognition system able to process simple and complex static gestures. In order to overcome problems inherent to PNN – primarily, slow execution with large training data sets – the proposed gesture recognition system uses clustering ensemble to reduce the training data set without significant deterioration of the quality of training. The reduction of training data set is efficiently performed using three types of clustering algorithms, yielding small number of input vectors that represent the original population very well.
Findings
The proposed methodology is capable of providing efficient recognition of simple and complex static gestures and was also successfully tested with gestures of an unseen user, i.e. person who took no part in the training phase.
Practical implications
The hand gesture recognition system based on the proposed methodology enables the use of affordable data gloves with a small number of sensors in VR engineering applications which require complex static gestures, including assembly and maintenance simulations.
Originality/value
According to literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a 5-sensor data glove.
Keywords
Acknowledgements
The research leading to these results has been partially supported by the European Community's Research Infrastructure Action – grant agreement VISIONAIR 262044 – under the 7th Framework Programme (FP7/2007-2013).
Citation
Luzanin, O. and Plancak, M. (2014), "Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network", Assembly Automation, Vol. 34 No. 1, pp. 94-105. https://doi.org/10.1108/AA-03-2013-020
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited