상세정보
학술대회
Human Touching Behavior Recognition Based on Neural Networks
- 발행일
- 200706
- 출처
- International Symposium on Neural Networks (ISNN) 2007 (LNCS 4492), v.4492, pp.730-739
- 협약과제
- 06MI1400, 능동형 서비스를 위한 URC 서버 프레임웍 개발, 조영조
- 초록
- Of the possible interactions between human and robot, touch is an important means of providing human beings with emotional relief. However, most previous studies have focused on interactions based on voice and images. In this paper, a method of recognizing human touching behaviors is proposed for developing a robot that can naturally interact with humans through touch. In this method, the recognition process is divided into pre-process phase and recognition phase. In the pre-process phase, recognizable characteristics are calculated from the data generated by the touch detector which was fabricated using force sensors. The force sensor used an FSR (force sensing register). The recognition phase classifies human touching behaviors using a multi-layer perceptron which is a neural network model. We measured three different human touching behaviors for six men. The human touching behaviors are 'hitting,' 'stroking,' and 'tickling'. In the test conducted with recognizers generated for each user, the average recognition rate was 93.8%, while the test conducted with a single recognizer showed a 79.8% average recognition rate. These results show the feasibility of the proposed human touching behavior recognition method. © Springer-Verlag Berlin Heidelberg 2007.
- KSP 제안 키워드
- Force Sensor, Recognition method, Recognition rate, behavior recognition, force sensing, multilayer perceptron, neural network model