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Sensors 2016, 16(2), 153; doi:10.3390/s16020153

Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking

1
The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56125 Pisa, Italy
2
Department of Movement, Human and Health Sciences, Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome “Foro Italico”, Piazza Lauro de Bosis 15, 00135 Roma, Italy
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 22 November 2015 / Revised: 16 January 2016 / Accepted: 19 January 2016 / Published: 26 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1984 KB, uploaded 26 January 2016]   |  

Abstract

Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm. To address this issue, a benchmarking procedure is presented, where simulated and real data are combined in different scenarios in order to quantify how each sensor’s uncertainties influence the accuracy of the final result. The proposed procedure was applied to the estimation of the pelvis orientation using a waist-worn magnetic-inertial measurement unit. Ground-truth data were obtained from a stereophotogrammetric system and used to obtain simulated data. Two Kalman-based sensor fusion algorithms were submitted to the proposed benchmarking procedure. For the considered application, gyroscope uncertainties proved to be the main error source in orientation estimation accuracy for both tested algorithms. Moreover, although different performances were obtained using simulated data, these differences became negligible when real data were considered. The outcome of this evaluation may be useful both to improve the design of new sensor fusion methods and to drive the algorithm tuning process. View Full-Text
Keywords: sensor fusion; algorithm benchmarking; inertial-magnetic sensors; human motion tracking; orientation; locomotion sensor fusion; algorithm benchmarking; inertial-magnetic sensors; human motion tracking; orientation; locomotion
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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

Ligorio, G.; Bergamini, E.; Pasciuto, I.; Vannozzi, G.; Cappozzo, A.; Sabatini, A.M. Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking. Sensors 2016, 16, 153.

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