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Gesture Recognition for Virtual Reality Applications Using Data Gloves and Neural Networks
This paper explores the use of hand gestures as a means of human-computer interactions for virtual reality applications. For the application, specific hand gestures such as "fist", "index finger", and "victory sign", have been defined. Most existing approaches use various camera-based recognition systems, which are rather costly and very sensitive to environmental changes.
In contrast, this paper explores a data glove as the input device, which provides 18 measurement values for the angles of different finger joints. This paper compares the performance of different neural network models, such as back-propagation and radial-basis functions, which are used by the recognition system to recognize the actual gesture.
Some network models achieve a recognition rate (training as well as generalization) of up to 100% over a number of test subjects. Due to its good performance, this recognition system is the first step towards virtual reality applications in which program execution is controlled by a sign language.
To view the complete paper, please download in PDF format.
John Weissmann
Department of Computer Science
University of Zurich
jody@ifi.unizh.ch
Ralf Salomon
Department of Computer Science
University of Zurich
salomon@ifi.unizh.ch
Copyright © 1999 Institute of Electrical and Electronics Engineers.
Reprinted from "Proceedings of the 1999 International Joint Conference on Neural Networks (Washington, DC, July 10-16, 1999)"
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