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Mathematical modeling of the human smooth pursuit system
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This licentiate thesis concerns mathematical modeling and identification of the the human smooth pursuit system (SPS) and the application of the models to motor symptom quantification in Parkinson's disease (PD).

The SPS is a complex neuromuscular system governing smooth pursuit eye movements (SPEM), and the task is to keep a moving target in the visual field.

Diagnosing and quantifying the disease is done by interview and clinical observation which requires hours of interaction between the patient and a qualified clinician. Acquiring a better understanding of the SPS cast in mathematical models may be a first step towards developing a technology that allows for fast and automatic PD staging.

Lately, the increased performance and accessibility of eye tracking technologies have generated a great deal of interest in the commercial sector. This thesis presents an effort towards developing more sophisticated data analysis techniques in an attempt to extract previously hidden information from the eye tracking data and to open up for new more advanced applications.

The SPS relates gaze direction to visual stimuli and may thus be viewed as a dynamical system with an input and an output signal. This thesis considers various parametric and non-parametric black- and grey-box models, both linear and nonlinear, to portray the SPS. The models are evaluated to characterize the SPS in different individuals and to look for discrepancies between the SPS function of healthy controls and Parkinson patients. It is shown that disease does indeed impair the system and that the effects are distinguishable from those of healthy aging.

Place, publisher, year, edition, pages
Uppsala University, 2014.
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2014-001
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
URN: urn:nbn:se:uu:diva-226170OAI: oai:DiVA.org:uu-226170DiVA: diva2:724406
Presentation
2014-01-21, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Supervisors
Available from: 2014-01-21 Created: 2014-06-12 Last updated: 2017-08-31Bibliographically approved
List of papers
1. Mathematical modeling and grey-box identification of the human smooth pursuit mechanism
Open this publication in new window or tab >>Mathematical modeling and grey-box identification of the human smooth pursuit mechanism
2010 (English)In: Proc. International Conference on Control Applications: CCA 2010, Piscataway, NJ: IEEE , 2010, 1023-1028 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2010
National Category
Control Engineering Information Science
Identifiers
urn:nbn:se:uu:diva-136531 (URN)10.1109/CCA.2010.5611079 (DOI)000286943900123 ()978-1-4244-5362-7 (ISBN)
Available from: 2010-12-13 Created: 2010-12-13 Last updated: 2014-06-12Bibliographically approved
2. Visual stimulus design in parameter estimation of the human smooth pursuit system from eye-tracking data
Open this publication in new window or tab >>Visual stimulus design in parameter estimation of the human smooth pursuit system from eye-tracking data
2013 (English)In: Proc. American Control Conference: ACC 2013, American Automatic Control Council , 2013, 887-892 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
American Automatic Control Council, 2013
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-213879 (URN)10.1109/ACC.2013.6579948 (DOI)000327210201010 ()978-1-4799-0177-7 (ISBN)
Conference
ACC 2013, June 17-19, Washington, DC
Available from: 2013-06-19 Created: 2014-01-05 Last updated: 2014-07-25Bibliographically approved
3. Non-parametric analysis of eye-tracking data by anomaly detection
Open this publication in new window or tab >>Non-parametric analysis of eye-tracking data by anomaly detection
2013 (English)In: Proc. 12th European Control Conference, IEEE , 2013, 632-637 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2013
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-224182 (URN)000332509701005 ()978-3-033-03962-9 (ISBN)
Conference
ECC 2013, July 17-19, Zürich, Switzerland
Available from: 2013-07-19 Created: 2014-05-05 Last updated: 2014-06-12Bibliographically approved
4. Parametric and non-parametric stochastic anomaly detection in analysis of eye-tracking data
Open this publication in new window or tab >>Parametric and non-parametric stochastic anomaly detection in analysis of eye-tracking data
2013 (English)In: Proc. 52nd Conference on Decision and Control, Piscataway, NJ: IEEE , 2013, 2532-2537 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2013
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-226160 (URN)10.1109/CDC.2013.6760261 (DOI)978-1-4673-5714-2 (ISBN)
Conference
CDC 2013, December 10-13, Florence, Italy
Available from: 2014-03-07 Created: 2014-06-12 Last updated: 2014-06-12Bibliographically approved
5. Volterra modeling of the smooth pursuit system with application to motor symptoms characterization in Parkinson's disease
Open this publication in new window or tab >>Volterra modeling of the smooth pursuit system with application to motor symptoms characterization in Parkinson's disease
2014 (English)In: 2014 European Control Conference (ECC), IEEE , 2014, 1856-1861 p.Conference paper, Published paper (Refereed)
Abstract [en]

A new way of modeling the Smooth Pursuit System (SPS) in humans by means of Volterra series expansion is suggested and utilized together with Gaussian Mixture Models (GMMs) to successfully distinguish between healthy controls and Parkinson patients based on their eye movements. To obtain parsimonious Volterra models, orthonormal function expansion of the Volterra kernels in Laguerre functions with the coefficients estimated by SParse Iterative Covariance-based Estimation (SPICE) is used. A combination of these two techniques is shown to greatly reduce the number of model parameters without significant performance loss. In fact, the resulting models outperform the Wiener models of previous research despite the significantly lower number of model parameters. Furthermore, the results of this study indicate that the nonlinearity of the system is likely to be dynamical in nature, rather than static which was previously presumed. The difference between the SPS in healthy controls and Parkinson patients is shown to lie largely in the higher order dynamics of the system. Finally, without the model reduction provided by SPICE, the GMM estimation fails, rendering the model unable to separate healthy controls from Parkinson patients.

Place, publisher, year, edition, pages
IEEE, 2014
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-226162 (URN)10.1109/ECC.2014.6862207 (DOI)000349955702026 ()978-3-9524269-1-3 (ISBN)
Conference
13th European Control Conference (ECC), JUN 24-27, 2014, Strasbourg, France
Available from: 2014-06-27 Created: 2014-06-12 Last updated: 2015-04-17Bibliographically approved

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Jansson, Daniel

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