Non-verbal Natural Interactive Human Intention Estimation Using Time-varying Fuzzy Markov Models

• Main Authors: Chang-En Yang, 楊長恩
• Other Authors: Peter Liu
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/62844575681044861638

碩士 === 淡江大學 === 電機工程學系碩士班 === 100 === The estimation of human intention for robot decision mechanism is the ultimate goal of this thesis. The human decision mechanism most information to exist the non-verbal language in the human communication. If the human robot interaction via the human intention of non-verbal language estimation and analysis the information then the robot decision mechanism will be similarity the human thinking and reaction. Therefore, we propose time-varying fuzzy Markov model to estimate the human intention of meaning of posture. We will via MATLAB simulation the intention states of the hands touch body location purport the intention probability of behavior and emphasize the time-varying model into the intention inference system will be better then time-invariant inference system, because the human thinking will be varies with time. In this thesis, we establish a time-varying fuzzy Markov model to estimate human intention for natural non-verbal human robot interface. Based on human posture information, we change the probability between states to improve the accuracy of estimation of human intention. The advantages of the approach are three fold: i) non-verbal information is core of natural interaction; ii) time-varying probability improves estimation accuracy; and iii) fuzzy inference consider practical human experience.