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research-article

Ground Reaction Force Estimation in Prosthetic Legs with Nonlinear Kalman Filtering Methods

[+] Author and Article Information
Seyed Abolfazl Fakoorian

Graduate Research Assistants Department of Electrical Engineering and Computer Science Cleveland State University Cleveland, Ohio, 44115
s.fakoorian@csuohio.edu

Vahid Azimi

Graduate Research Assistants Department of Electrical Engineering and Computer Science Cleveland State University Cleveland, Ohio, 44115
v.azimi@csuohio.edu

Mahmoud Moosavi

Graduate Research Assistants Department of Electrical Engineering and Computer Science Cleveland State University Cleveland, Ohio, 44115
s.moosavi17@csuohio.edu

Hanz Richter

Associate Professor Department of Mechanical Engineering Cleveland State University Cleveland, Ohio, 44115
h.richter@csuohio.edu

Dan Simon

Professor Department of Electrical Engineering and Computer Science Cleveland State University Cleveland, Ohio, 44115
d.j.simon@csuohio.edu

1Corresponding author.

ASME doi:10.1115/1.4036546 History: Received December 06, 2016; Revised March 10, 2017

Abstract

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system, but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

Copyright (c) 2017 by ASME
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