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Sensors 2016, 16(2), 235; doi:10.3390/s16020235

Inertial Sensor Error Reduction through Calibration and Sensor Fusion

1
Neural Rehabilitation Group, CSIC, Av. Dr. Arce 37, Madrid 28002, Spain
2
Division PMA, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Celestijnenlaan 300B, B-3001 Heverlee, Belgium
3
Department of Biomedical Kinesiology, Katholieke Universiteit Leuven, Tervuursevest 101, B-3001 Heverlee, Belgium
4
Department of Electrical Engineering, Federal University of São Carlos, São Paulo 13565-905, Brazil
5
Department of Mechanical Engineering, Center for Robotics of São Carlos, University of São Paulo, São Paulo 13565-905, Brazil
6
Department of Electrical Engineering, Center for Robotics of São Carlos, University of São Paulo, São Paulo 13565-905, Brazil
7
Group of Neural and Cognitive Engineering, CSIC, Ctra. de Campo Real km 0.200, Arganda del Rey 28500, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Gert F. Trommer
Received: 6 November 2015 / Revised: 15 January 2016 / Accepted: 4 February 2016 / Published: 17 February 2016
(This article belongs to the Special Issue Inertial Sensors and Systems)
View Full-Text   |   Download PDF [9040 KB, uploaded 17 February 2016]   |  

Abstract

This paper presents the comparison between cooperative and local Kalman Filters (KF) for estimating the absolute segment angle, under two calibration conditions. A simplified calibration, that can be replicated in most laboratories; and a complex calibration, similar to that applied by commercial vendors. The cooperative filters use information from either all inertial sensors attached to the body, Matricial KF; or use information from the inertial sensors and the potentiometers of an exoskeleton, Markovian KF. A one minute walking trial of a subject walking with a 6-DoF exoskeleton was used to assess the absolute segment angle of the trunk, thigh, shank, and foot. The results indicate that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration. The interaction between filter and calibration suggests that when the quality of the calibration is unknown the Markovian KF is recommended. Applying the complex calibration, the Matricial and Markovian KF perform similarly, with average RMSE below 1.22 degrees. Cooperative KFs perform better or at least equally good as Local KF, we therefore recommend to use cooperative KFs instead of local KFs for control or analysis of walking. View Full-Text
Keywords: inertial sensor; Kalman Filter; motion analysis; biomechanics; exoskeleton; calibration inertial sensor; Kalman Filter; motion analysis; biomechanics; exoskeleton; calibration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lambrecht, S.; Nogueira, S.L.; Bortole, M.; Siqueira, A.A.G.; Terra, M.H.; Rocon, E.; Pons, J.L. Inertial Sensor Error Reduction through Calibration and Sensor Fusion. Sensors 2016, 16, 235.

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