Optimal Estimation of Supporting-Ground Orientation for Multi-Segment Body Based on Otolith-Canal Fusion
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.
[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
[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
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:63547", author = "Karim A. Tahboub", title = "Optimal Estimation of Supporting-Ground Orientation for Multi-Segment Body Based on Otolith-Canal Fusion", 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.", volume = "4", number = "3", pages = "334-8", }