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Sensors 2015, 15(9), 23983-24001; doi:10.3390/s150923983

How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy

1
Interuniversity Center of Bioengineering of the Human Neuromusculoskeletal System, Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Piazza Lauro de Bosis 15, 00135 Roma, Italy
2
The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 22 July 2015 / Revised: 9 September 2015 / Accepted: 14 September 2015 / Published: 18 September 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2550 KB, uploaded 21 September 2015]   |  

Abstract

In human movement analysis, 3D body segment orientation can be obtained through the numerical integration of gyroscope signals. These signals, however, are affected by errors that, for the case of micro-electro-mechanical systems, are mainly due to: constant bias, scale factor, white noise, and bias instability. The aim of this study is to assess how the orientation estimation accuracy is affected by each of these disturbances, and whether it is influenced by the angular velocity magnitude and 3D distribution across the gyroscope axes. Reference angular velocity signals, either constant or representative of human walking, were corrupted with each of the four noise types within a simulation framework. The magnitude of the angular velocity affected the error in the orientation estimation due to each noise type, except for the white noise. Additionally, the error caused by the constant bias was also influenced by the angular velocity 3D distribution. As the orientation error depends not only on the noise itself but also on the signal it is applied to, different sensor placements could enhance or mitigate the error due to each disturbance, and special attention must be paid in providing and interpreting measures of accuracy for orientation estimation algorithms. View Full-Text
Keywords: 3D orientation; MEMS gyroscopes; noise sources; motion analysis; gait; biomechanics; human; numerical integration; inertial sensors 3D orientation; MEMS gyroscopes; noise sources; motion analysis; gait; biomechanics; human; numerical integration; inertial sensors
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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

Pasciuto, I.; Ligorio, G.; Bergamini, E.; Vannozzi, G.; Sabatini, A.M.; Cappozzo, A. How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy. Sensors 2015, 15, 23983-24001.

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