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Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation
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.
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.ORCID iD: 0000-0002-6608-250x
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. p. 4692-4697
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-334957DOI: 10.1109/CDC.2017.8264352ISI: 000424696904084ISBN: 978-1-5090-2873-3 (electronic)OAI: oai:DiVA.org:uu-334957DiVA, id: diva2:1161244
Conference
CDC 2017, December 12–15, Melbourne, Australia
Funder
VinnovaAvailable from: 2018-01-23 Created: 2017-11-29 Last updated: 2019-06-18Bibliographically approved
In thesis
1. Volterra modeling of the human smooth pursuit system in health and disease
Open this publication in new window or tab >>Volterra modeling of the human smooth pursuit system in health and disease
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:nbn:se:uu:diva-385951 (URN)
Supervisors
Available from: 2019-05-08 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved

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Bro, ViktorMedvedev, Alexander

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