An IMU tracking algorithm for 3D motion reconstruction using OpenSim dynamical models

Implementation on a robot manipulator

Master thesis (2021)

Authors

P.A.M. de Kanter Mechanical Engineering

Contributors

M. Kok Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (supervisor 1)
A. Seth Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
L. Peternel Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (supervisor 2)
H.E.J. Veeger Biomechanical Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2021 Daan de Kanter
Kalman Filtering State Estimation OpenSim Biomechanical Model IMU Sensor Fusion 3D Motion Reconstruction
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Published Date

17-02-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

Inertial Measurement Unit (IMU)-based motion capture has gained interest over the years due to its potential to measure human movement in the clinic and on the sports field at low cost. Still, IMU-based motion reconstruction remains a challenging task as these IMU measurements are corrupted by noise and bias. There have been many filtering and sensor fusion algorithms developed to address this problem, but limited attention has been paid to including the system dynamics of the subject to estimate kinematics and kinetics from multiple IMUs. I propose a novel motion reconstruction algorithm for capturing 3D motions in a markerless and unconstrained environment using gyroscope and accelerometer measurements. The algorithm is based upon an Iterated Extended Kalman Filter for state estimation and the 3D dynamical model of the subject created in OpenSim (IEKF-OS). The IEKF-OS algorithm consists of two stages. The first stage incorporates the dynamics of the subject to predict the motion. The second stage tracks measurements from multiple IMUs to improve model estimates of joint angles and speeds. The goal of this thesis is threefold. Firstly, the IEKF-OS is derived and verified using simulations performed on a six-link robot manipulator including sensor placement errors. Secondly, experiments are performed to validate the algorithm’s estimations against the robot’s ground truth joint encoder values. Thirdly, the algorithm is compared against an inverse kinematics method to track IMU estimated orientations (OpenSense) provided by OpenSim. The IEKF-OS algorithm showed lower motion tracking errors compared to OpenSense for various motions performed by the robot manipulator. The joint angle estimations as computed by both methods are compared against the gold-standard ground truth robot encoder values. OpenSense joint angle values were in the range of 0.6-6.4 [deg] RMSE, whereas the IEKF-OS algorithm estimated joint angles and speeds in the range of 0.4-2.8 [deg] and 0.3-2 [deg/s] RMSE, respectively. These results highlight accurate 3D motion reconstruction on a six-link robot manipulator. Contrary to OpenSense, the IEKF-OS is able to accurately reconstruct motions regardless of magnetic disturbances because it does not rely on magnetometer data.