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Non-parametric analysis of eye-tracking data by anomaly detection
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.
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.
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. 632-637 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-224182ISI: 000332509701005ISBN: 978-3-033-03962-9 (print)OAI: oai:DiVA.org:uu-224182DiVA: diva2:716132
Conference
ECC 2013, July 17-19, Zürich, Switzerland
Available from: 2013-07-19 Created: 2014-05-05 Last updated: 2014-06-12Bibliographically approved
In thesis
1. Mathematical modeling of the human smooth pursuit system
Open this publication in new window or tab >>Mathematical modeling of the human smooth pursuit system
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:nbn:se:uu:diva-226170 (URN)
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

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Jansson, DanielRosén, OlovMedvedev, Alexander

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