Hidden Markov Model for Gesture Recognition

Abstract

This report presents a method for developing a gesture-based system using a multi-dimensional hidden Markov model (HMM). Instead of using geometric features, gestures are converted into sequential symbols. HMMs are employed to represent the gestures and their parameters are learned from the training data. Based on the most likely performance criterion, the gestures can be recognized through evaluating the trained HMMs. We have developed a prototype system to demonstrate the feasibility of the proposed method. The system achieved 99.78% accuracy for an isolated recognition task with nine gestures. Encouraging results were also obtained from experiments of continuous gesture recognition. The proposed method is applicable to any gesture represented by a multi- dimensional signal, and will be a valuable tool in telerobotics and human computer interfaces

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1994
Accession Number
ADA282845

Entities

People

  • Jie Yang
  • Yangsheng Xu

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Automated Speech Recognition
  • Feature Extraction
  • Formal Languages
  • Heuristic Methods
  • Hidden Markov Models
  • Human-Machine Interaction
  • Language
  • Markov Models
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Random Variables
  • Recognition
  • Stochastic Processes
  • Two Dimensional

Fields of Study

  • Computer science
  • Engineering

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Speech Processing/Speech Recognition.