Accurate, robust, and real-time estimation of finger pose with a motion capture system

Finger exoskeletons, haptic devices, and augmented reality applications demand 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 system identification and state estimation in a unified framework. The system identification stage investigates the accurate model of a finger, and the state estimation 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 experiment. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 Khz) in presence of measurement noise, occlusion of markers, and fast movement.