Error-State Kalman Filter for Online Evaluation of Ankle Angle

Abstract

This paper presents an Error-State Kalman Filter (ESKF) for state estimation in a 2-DOF robotic prosthetic ankle. The filter estimates the ankle angle in inversion-eversion (IE), external-internal (EI), and dorsiflexion-plantarflexion (DP), using measurements from two low-cost magnetic, angular rate, and gravity sensor modules (MARGs), also known as 9-axis Inertial Measurement Units (IMUs). To this end, we transformed raw MARG measurements into body frames and modeled the states and constraints of the 2-DOF robotic prosthesis in an Error State Kalman Filter (ESKF). Experimental tests showed the proposed ESKF provided better results than the Madgwick filter, a commonly used attitude estimator. The proposed filter is developed for ankle prostheses requiring direct angle measurement and can be expanded to an online evaluation of ankle angle in humans.