Hand Gesture Recognition Using Neural Networks.

Abstract

Gestural interfaces have the potential of enhancing control operations in numerous applications. For Air Force systems, machine-recognition of whole-hand gestures may be useful as an alternative controller, especially when conventional controls are less accessible. The objective of this effort was to explore the utility of a neural network-based approach to the recognition of whole-hand gestures. Using a fiber-optic instrumented glove, gesture data were collected for a set of static gestures drawn from the manual alphabet used by the deaf. Two types of neural networks (multilayer perceptron and Kohonen self-organizing feature map) were explored. Both showed promise, but the perceptron model was quicker to implement and classification is inherent in the model. The high gesture recognition rates and quick network retraining times found in the present study suggest that a neural network approach to gesture recognition be further evaluated.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1996
Accession Number
ADA314933

Entities

People

  • Edward L. Fix
  • Gloria L. Calhoun
  • Paul R. Morton

Organizations

  • Armstrong Laboratory

Tags

DTIC Thesaurus Topics

  • Air Force
  • Alphabets
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Classification
  • Human-Machine Interaction
  • Neural Networks
  • Recognition
  • Retraining

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Networking
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks