Ambulatory Position Tracking of Prosthetic Limbs Using Multiple Satellite Aided Inertial Sensors and Adaptive Mixing

Abstract

In this study, satellite based pseudo-range measurements are integrated with accelerometer measurements made by six accelerometers located on the six faces of a cuboid, to independently measure the translational and rotational accelerations, and the pseudo-range. These measurements are then processed by an adaptive Unscented Kalman filter (UKF) to correct for the estimated errors and to obtain the required position and velocities at two independent locations. The relative position and velocity are then obtained by the application of standard vector identities. From these estimates, the position and velocity kinematics of prosthetic limbs and measurements of the joint angles, the true ambulatory position is estimated using a third independent UKF based estimator. The robotic limb joint offsets are assumed to be biased which are also estimated by the third UKF. The basic assumption is that the errors in the measurements are quite similar at the two locations and for this reason it is hypothesised that these errors would be reduced when the relative position and velocity were estimated. The results indicate that the steady-state ambulatory position error of the end-effector is reduced by more than 98%.