Many robotic applications need an accurate, robust, and fast estimation of finger pose. We present a novel finger pose estimation method using a motion capture system. The method combines a system identification stage and a state tracking stage in a unified framework. The system identification stage develops an accurate model of a finger, and the state tracking stage tracks the finger pose with the extended Kalman filter (EKF) algorithm based on the model obtained in the system identification stage. The algorithm is validated by simulation, and experiments with a human subject and a robotic finger. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 kHz) in the presence of measurement noise, occlusion of markers, and fast movement.

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