Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11923
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Main Title: Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All
Author(s): Caruso, Marco
Sabatini, Angelo Maria
Laidig, Daniel
Seel, Thomas
Knaflitz, Marco
Della Croce, Ugo
Cereatti, Andrea
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/13129
http://dx.doi.org/10.14279/depositonce-11923
License: https://creativecommons.org/licenses/by/4.0/
Abstract: The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.
Subject(s): MIMU
orientation estimation
filter parameters
filter comparison
wearable sensors
sensor fusion
human motion
Kalman filters
complementary filters
optimal parameters
Issue Date: 5-Apr-2021
Date Available: 19-May-2021
Language Code: en
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Sponsor/Funder: EC/H2020/820820/EU/Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement/MOBILISE-D
Journal Title: Sensors
Publisher: MDPI
Volume: 21
Issue: 7
Article Number: 2543
Publisher DOI: 10.3390/s21072543
EISSN: 1424-8220
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Energie- und Automatisierungstechnik » FG Regelungssysteme
Appears in Collections:Technische Universität Berlin » Publications

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