다변량 퍼지 의사결정트리와 사용자 적응을 이용한 손동작 인식 (Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation)

서지정보

· 발행기관 : 한국로봇학회
· 수록지 정보 : 로봇학회 논문지 / 3권 / 2호 / 81 ~ 90페이지
· 저자명 : 전문진, 도준형, 이상완, 박광현, 변증남

초록

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.

영어초록

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.

참고자료

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