A real-time hand gesture interface for medical visualization applications

Juan Wachs, Helman Stern, Yael Edan, Michael Gillam, Craig Feied, Mark Smith, Jon Handler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

33 Scopus citations

Abstract

In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accom-plished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features, and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.

Original languageEnglish
Title of host publicationApplications of Soft Computing
Subtitle of host publicationRecent Trends
Pages153-162
Number of pages10
DOIs
StatePublished - 1 Dec 2006

Publication series

NameAdvances in Soft Computing
Volume36
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

Keywords

  • Computerized databases
  • Fuzzy c-means
  • Haar features
  • Hand gesture recognition
  • Neighborhood search

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications

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