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An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to...
The mass parameters of the human body segments are important when studying motion dynamics and the in-vivo method to obtain accurate parameters is required in biomechanics studies and for some medical applications. In our previous works, we proposed the method to identify inertial parameters of human body segments in real-time during measurement of motion. However, some obtained parameters are not...
This paper proposes an approach to construct a system which allows humanoid robots to recognize human behaviors and predict his or her future behaviors. The system consists of two modules : “motion symbol tree” and “motion symbol graph”, Human demonstrator motion patterns are stored as motion symbols, which abstract the motion data by using Hidden Markov Models. The stored motion patterns are organized...
This paper describes an approach to structuring behavioral knowledge based on classification of human whole body motions and extraction of the behavioral transitions. The motion patterns are learned by Hidden Markov Models (HMMs), which can be used for classification of the motion patterns. The HMMs are called “motion symbol” since They abstract their corresponding motion patterns. The motion patterns...
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