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Volterra modeling of the human smooth pursuit system in health and disease
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.
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis treats the identification of Volterra models of the human smooth pursuit system from eye-tracking data. Smooth pursuit movements are gaze movements used in tracking of moving targets and controlled by a complex biological network involving the eyes and brain. Because of the neural control of smooth pursuit, these movements are affected by a number of neurological and mental conditions, such as Parkinson's disease. Therefore, by constructing mathematical models of the smooth pursuit system from eye-tracking data of the patient, it may be possible to identify symptoms of the disease and quantify them. While the smooth pursuit dynamics are typically linear in healthy subjects, this is not necessarily true in disease or under influence of drugs. The Volterra model is a classical black-box model for dynamical systems with smooth nonlinearities that does not require much a priori information about the plant and thus suitable for modeling the smooth pursuit system.

The contribution of this thesis is mainly covered by the four appended papers. Papers I–III treat the problem of reducing the number of parameters in Volterra models with the kernels parametrized in Laguerre functional basis (Volterra–Laguerre models), when utilizing them to capture the signal form of smooth pursuit movements. Specifically, a Volterra–Laguerre model is obtained by means of sparse estimation and principal component analysis in Paper I, and a Wiener model approach is used in Paper II. In Paper III, the same model as in Paper I is considered to examine the feasibility of smooth pursuit eye tracking for biometric purposes. Paper IV is concerned with a Volterra–Laguerre model that includes an explicit time delay. An approach to the joint estimation of the time delay and the finite-dimensional part of the Volterra model is proposed and applied to time-delay compensation in eye-tracking data.

Place, publisher, year, edition, pages
Uppsala University, 2019.
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2019-003
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
URN: urn:nbn:se:uu:diva-385951OAI: oai:DiVA.org:uu-385951DiVA, id: diva2:1326625
Supervisors
Available from: 2019-05-08 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved
List of papers
1. Constrained SPICE in Volterra–Laguerre modeling of human smooth pursuit
Open this publication in new window or tab >>Constrained SPICE in Volterra–Laguerre modeling of human smooth pursuit
2017 (English)In: Proc. 1st Conference on Control Technology and Applications, IEEE, 2017, p. 13-18Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-334955 (URN)10.1109/CCTA.2017.8062433 (DOI)000426981500003 ()978-1-5090-2182-6 (ISBN)
Conference
CCTA 2017, August 27–30, Mauna Lani, HI
Funder
Vinnova
Available from: 2017-10-09 Created: 2017-11-29 Last updated: 2019-06-18Bibliographically approved
2. Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation
Open this publication in new window or tab >>Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation
2017 (English)In: Proc. 56th Conference on Decision and Control, IEEE, 2017, p. 4692-4697Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-334957 (URN)10.1109/CDC.2017.8264352 (DOI)000424696904084 ()978-1-5090-2873-3 (ISBN)
Conference
CDC 2017, December 12–15, Melbourne, Australia
Funder
Vinnova
Available from: 2018-01-23 Created: 2017-11-29 Last updated: 2019-06-18Bibliographically approved
3. Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters
Open this publication in new window or tab >>Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters
2019 (English)In: Article in journal (Other academic) Submitted
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-385940 (URN)
Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved
4. Identification of continuous Volterra models with explicit time delay through series of Laguerre functions
Open this publication in new window or tab >>Identification of continuous Volterra models with explicit time delay through series of Laguerre functions
2019 (English)Conference paper, Published paper (Other academic)
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-385943 (URN)
Note

submitted

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved

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  • apa
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Output format
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