The purpose of this research is to calibrate sets of kinematics and dynamic parameters of human arm from its captured points via many behaviors. If both sets are successively identified, we can easily generate dynamic animation of human such as handling, pitching, walking, running and so on. In addition, we can straightforwardly touch a virtual arm or leg while feeling reactive force/moment by a haptic rendering (tactile feedback display). In robotics, many types of algorithms have been

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... for calibrating kinematics and dynamic parameters of robotic manipulator. They are meaningless for calibrating a human arm as follows: (1) we do not know any control scheme and feedback gain. (2) We do not know any torque of each actuator. (3) We do not know any set of good initial parameters of kinematics and dynamics. To overcome these, we propose a probabilistic algorithm to calibrate sets of kinematics and dynamic parameters of a human arm from its successive captured points. The calibration algorithm based on GA (Genetic Algorithm) does not require any set of good initial set of kinematics and dynamic parameters.