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

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...

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Autores principales: Ligorio, Gabriele, Bergamini, Elena, Pasciuto, Ilaria, Vannozzi, Giuseppe, Cappozzo, Aurelio, Sabatini, Angelo Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801531/
https://www.ncbi.nlm.nih.gov/pubmed/26821027
http://dx.doi.org/10.3390/s16020153
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author Ligorio, Gabriele
Bergamini, Elena
Pasciuto, Ilaria
Vannozzi, Giuseppe
Cappozzo, Aurelio
Sabatini, Angelo Maria
author_facet Ligorio, Gabriele
Bergamini, Elena
Pasciuto, Ilaria
Vannozzi, Giuseppe
Cappozzo, Aurelio
Sabatini, Angelo Maria
author_sort Ligorio, Gabriele
collection PubMed
description 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.
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spelling pubmed-48015312016-03-25 Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking Ligorio, Gabriele Bergamini, Elena Pasciuto, Ilaria Vannozzi, Giuseppe Cappozzo, Aurelio Sabatini, Angelo Maria Sensors (Basel) Article 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. MDPI 2016-01-26 /pmc/articles/PMC4801531/ /pubmed/26821027 http://dx.doi.org/10.3390/s16020153 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ligorio, Gabriele
Bergamini, Elena
Pasciuto, Ilaria
Vannozzi, Giuseppe
Cappozzo, Aurelio
Sabatini, Angelo Maria
Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking
title Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking
title_full Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking
title_fullStr Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking
title_full_unstemmed Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking
title_short Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking
title_sort assessing the performance of sensor fusion methods: application to magnetic-inertial-based human body tracking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801531/
https://www.ncbi.nlm.nih.gov/pubmed/26821027
http://dx.doi.org/10.3390/s16020153
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