This work 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 to 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 on humans.

Researchers should cite this work as follows:

• Soliman, A. K.; Ribeiro, G. A.; Torres, A.; Rastgaar, M. (2022). Error-State Kalman Filter for Online Evaluation of Ankle Angle. Purdue University Research Repository. doi:10.4231/X00Q-YW81

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1. Acceleration
2. Biomedical Engineering
3. Biomedical Engineering (Dept)
4. Engineering Technology
5. Matlab software
6. Mechanical Engineering
7. Robotics