Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Optimal Estimation of Supporting-Ground Orientation for Multi-Segment Body Based on Otolith-Canal Fusion

Authors: Karim A. Tahboub

Abstract:

This article discusses the problem of estimating the orientation of inclined ground on which a human subject stands based on information provided by the vestibular system consisting of the otolith and semicircular canals. It is assumed that body segments are not necessarily aligned and thus forming an open kinematic chain. The semicircular canals analogues to a technical gyrometer provide a measure of the angular velocity whereas the otolith analogues to a technical accelerometer provide a measure of the translational acceleration. Two solutions are proposed and discussed. The first is based on a stand-alone Kalman filter that optimally fuses the two measurements based on their dynamic characteristics and their noise properties. In this case, no body dynamic model is needed. In the second solution, a central extended disturbance observer that incorporates a body dynamic model (internal model) is employed. The merits of both solutions are discussed and demonstrated by experimental and simulation results.

Keywords: Kalman filter, orientation estimation, otolith-canalfusion, vestibular system.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083183

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1147

References:


[1] K. Cullen, and S. Sadeghi, "Vestibular system," Scholarpedia, http://www.scholarpedia.org/article/Vestibular_system, 2008.
[2] C.M. Oman, "A heuristic mathematical model for the dynamics of sensory conflict and motion sickness," Acta Otolaryngol Suppl, 1982, Vol. 392, pp. 1-44.
[3] S. Glasauer and D.M. Merfeld, " Modeling three dimensional vestibular responses during complex motion stimulations," in Three-dimensional kinematics of eye, head and limb movements. Harwood Switzerland, 1997, pp 387-389.
[4] R. Mayne, "A systems concept of the vestibular organs," in: Handbook of Sensory Physiology, vol. 4, Vestibular System Part 2: Psychophysics, Applied Aspects and General Interpretations, H.H. Kornhuber, Ed. Berlin: Springer, 197, pp. 493-580.
[5] T. Mergner and S. Glasauer, " A simple model of vestibular canalotolith signal fusion," Ann N Y. Acad Sci, 1999, Vol 871, pp. 430-434.
[6] J. Laurens and J. Droulez, ÔÇÿBayesian processing of vestibular information," Biological Cybernetics, 2007, vol. 96, pp. 389-404.
[7] P. Zhang, J. Gu, E.e. Milios, and P. Huhnh, ÔÇÿNavigation with imu/gps/digital compass with unscented Kalman filter,- IEEE International Conference on Mechatronics and Automation, 2005.
[8] C. Maurer, T. Mergner, and R.J. Peterka, ÔÇÿMultisensory control of human upright stance,- Experimental Brain Research, 2006, vol. 171, pp. 231-250.
[9] K. A. Tahboub, K. A., "Biologically-inspired humanoid postural control," Journal of Physiology - Paris, Vol. 103, pp. 195-214, 2009.
[10] L. Zupan, D. M. Merfeld, and C. Darlot, "Using sensory weighting to model the influence of canal, otolith and visual cues on spatial orientation and eye movements. Biol. Cybern., 86, 209-230, 2002.
[11] P.R. MacNeilage, N. Ganesan and D.E. Angelaki, "Computational Approaches to Spatial Orientation: From Transfer Functions to Dynamic Bayesian Inference," J Neurophysiol, 100, pp. 2981-2996, 2008.
[12] B. Friedland, "Control System Design: An Introduction to State-Space Methods," New York, NY: McGraw-Hill Book Company, 1987.
[13] R. Peterka "Sensorimotor integration in human postural control," J Neurophysiol (2002) vol. 88, pp. 1097-1118