Earticle

초록

영어

This paper proposes a novel 3D wrist gesture recognition method as the human-machine interface for mobile robot control. A sequence of depth maps was extracted from Kinect sensor on wrist gestures of an operator and a wrist gesture was specified by a sequence of 24-directional codes, which represents 3D motions with feature values of a single type, including directional and depth information. HMM algorithm was used to recognize wrist gestures, in which a fully connected hidden Markov model was designed and trained by Viterbi method and Baum-Welch method and the forward-backward procedure was used for the likelihood evaluation. The performance evaluation considering various factors showed above 96% of recognition rate and higher recognition rate on wrist gestures with complex motions.

목차

Abstract
 1. Introduction
 2. 3D Feature Values for Wrist Gestures
  2.1 Definition of Wrist Gestures for Control
  2.2 24-Diectional Codes for Wrist Gestures
 3. Recognition of Wrist Gestures Using HMM
  3.1 Design of Hidden Markov Model
  3.2 Training of HMM
  3.3 Evaluation and Recognition of HMM
 4. Performance Evaluation
 5. Conclusions
 References

키워드

저자

• Young-ju Kim [ Department of Computer Engineering, School of Engineering, Silla University, Busan 617-736, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605