Hand Gesture Recognition using Multivariate Fuzzy Decision Tree and User Adaptation

다변량 퍼지 의사결정트리와 사용자 적응을 이용한 손동작 인식

  • 전문진 (한국항공우주연구원 다목적 3 호체계팀) ;
  • 도준형 (한국과학기술원 인간친화 복지로봇시스템 연구센터) ;
  • 이상완 (한국과학기술원 전자전산학과) ;
  • 박광현 (광운대학교 정보제어공학과) ;
  • 변증남 (한국과학기술원 전자전산학과)
  • Published : 2008.05.30


While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.


Supported by : 한국과학재단