A disturbance rejection adaptive filtering approach for human motion tracking *

Abstract

Abstract This article studies the human motion tracking problem using the disturbance rejection adaptive filter with an inertial sensor. Due to the complexity of human motion, conventional inertial methods easily suffer from motion accelerations (MAs) and ferromagnetic disturbances (FDs). Here, a disturbance rejection adaptive filter is presented to segregate the exterior disturbances from the inertial sensor observations before attitude estimation. This method estimates the MA and FD by Kalman filters. According to disturbance intensity, Sage–Husa adaptive strategies based on fuzzy rules are designed to adjust noise covariance. Moreover, gravity and geomagnetic field estimation are applied as multiplicative extended Kalman filter observations to solve external disturbances problems. Finally, the effectiveness and superiority of the proposed method are verified by an example of human motion tracking.