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Jansson, Daniel
Publications (10 of 14) Show all publications
Medvedev, A. & Jansson, D. (2016). Capturing age-related alternations in the human smooth pursuit mechanism by Volterra models. In: 2016 AMERICAN CONTROL CONFERENCE (ACC): . Paper presented at American Control Conference (ACC), JUL 06-08, 2016, Boston, MA (pp. 1289-1294). IEEE
Open this publication in new window or tab >>Capturing age-related alternations in the human smooth pursuit mechanism by Volterra models
2016 (English)In: 2016 AMERICAN CONTROL CONFERENCE (ACC), IEEE , 2016, p. 1289-1294Conference paper, Published paper (Refereed)
Abstract [en]

A method for quantifying the effects of aging in the human smooth pursuit system is proposed. The dynamical properties of the oculomotor system are characterized by means of a truncated Volterra model that has previously been utilized for distinguishing between patients diagnosed with Parkinson's disease and healthy controls. The orthonormal basis of Laguerre functions is employed for the parameterization of the Volterra model kernels. The Volterra-Laguerre model coefficients are estimated from gaze direction data collected by means of eye tracking from healthy adults of different age responding to specially designed visual stimuli. The experimental results suggest that aging primarily impacts the linear term of the Volterra model through gain and frequency bandwidth reduction. Parameter variability increases with age, both in the linear and quadratic term of the Volterra model. The mean values of the nonlinear term coefficients appear though to be essentially independent of age within the studied population.

Place, publisher, year, edition, pages
IEEE, 2016
Series
Proceedings of the American Control Conference, ISSN 0743-1619
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-320120 (URN)000388376101055 ()978-1-4673-8682-1 (ISBN)
Conference
American Control Conference (ACC), JUL 06-08, 2016, Boston, MA
Funder
VINNOVA
Available from: 2017-04-21 Created: 2017-04-21 Last updated: 2018-01-13Bibliographically approved
Jansson, D. (2015). Identification Techniques for Mathematical Modeling of the Human Smooth Pursuit System. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Identification Techniques for Mathematical Modeling of the Human Smooth Pursuit System
2015 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

This thesis proposes nonlinear system identification techniques for the mathematical modeling of the human smooth pursuit system (SPS) with application to motor symptom quantification in Parkinson's disease (PD). The SPS refers to the complex neuromuscular system in humans that governs the smooth pursuit eye movements (SPEM). Insight into the SPS and its operation is of importance in a wide and steadily expanding array of application areas and research fields. The ultimate purpose of the work in this thesis is to attain a deeper understanding and quantification of the SPS dynamics and thus facilitate the continued development of novel commercial products and medical devices. The main contribution of this thesis is in the derivation and evaluation of several techniques for SPS characterization. While attempts to mathematically model the SPS have been made in the literature before, several key aspects of the problem have been previously overlooked.This work is the first one to devise dynamical models intended for extended-time experiments and also to consider systematic visual stimuli design in the context of SPS modeling. The result is a handful of parametric mathematical models outperforming current State-of-the-Art models in terms of prediction accuracy for rich input signals. As a complement to the parametric dynamical models, a non-parametric technique involving the construction of individual statistical models pertaining to specific gaze trajectories is suggested. Both the parametric and non-parametric models are demonstrated to successfully distinguish between individuals or groups of individuals based on eye movements.Furthermore, a novel approach to Wiener system identification using Volterra series is proposed and analyzed. It is exploited to confirm that the SPS in healthy individuals is indeed nonlinear, but that the nonlinearity of the system is significantly stronger in PD subjects. The nonlinearity in healthy individuals appears to be well-modeled by a static output function, whereas the nonlinear behavior introduced to the SPS by PD is dynamical.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. p. xiv+176
Series
Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-2516 ; 115
Keywords
nonlinear system identification, biomedical signal processing
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-264292 (URN)978-91-554-9367-7 (ISBN)
Public defence
2015-11-27, P2446, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2015-11-04 Created: 2015-10-08 Last updated: 2023-03-13
Jansson, D., Rosén, O. & Medvedev, A. (2015). Parametric and nonparametric analysis of eye-tracking data by anomaly detection. IEEE Transactions on Control Systems Technology, 23(4), 1578-1586
Open this publication in new window or tab >>Parametric and nonparametric analysis of eye-tracking data by anomaly detection
2015 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 23, no 4, p. 1578-1586Article in journal (Refereed) Published
Abstract [en]

An approach to smooth pursuit eye movement's analysis by means of stochastic anomaly detection is presented and applied to the problem of distinguishing between patients diagnosed with Parkinson's disease and normal controls. Both parametric Wiener model-based techniques and nonparametric modeling utilizing a description of the involved probability density functions in orthonormal bases are considered. The necessity of proper visual stimuli design for the accuracy of mathematical modeling is highlighted and a formal method for producing such stimuli is suggested. The efficacy of the approach is demonstrated on experimental data collected by means of a commercial video-based eye tracker.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-234494 (URN)10.1109/TCST.2014.2364958 (DOI)000356523600027 ()
Projects
Systeam
Funder
EU, European Research Council, 247035
Available from: 2015-06-16 Created: 2014-10-20 Last updated: 2017-12-05Bibliographically approved
Jansson, D., Medvedev, A., Axelson, H. & Nyholm, D. (2015). Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease. In: Signal and Image Analysis for Biomedical and Life Sciences: (pp. 63-82). Springer
Open this publication in new window or tab >>Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease
2015 (English)In: Signal and Image Analysis for Biomedical and Life Sciences, Springer, 2015, p. 63-82Chapter in book (Refereed)
Abstract [en]

Two methods for distinguishing between healthy controls and patients diagnosed with Parkinson's disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patients with Parkinson's disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects tracking visual stimuli. Both methods show promising results, where healthy controls and patients with Parkinson's disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson's disease.

Place, publisher, year, edition, pages
Springer, 2015
Series
Advances in Experimental Medicine and Biology, ISSN 0065-2598 ; 823
National Category
Control Engineering Neurology
Identifiers
urn:nbn:se:uu:diva-244824 (URN)10.1007/978-3-319-10984-8_4 (DOI)000350427300005 ()25381102 (PubMedID)978-3-319-10983-1 (ISBN)
Available from: 2014-10-13 Created: 2015-02-20 Last updated: 2015-04-16Bibliographically approved
Jansson, D. & Medvedev, A. (2015). System Identification of Wiener Systems via Volterra-Laguerre Models: Application to Human Smooth Pursuit Analysis. In: 2015 European Control Conference (Ecc): . Paper presented at 2015 EUROPEAN CONTROL CONFERENCE (ECC), July 15-17, Linz, Austria (pp. 2700-2705). IEEE
Open this publication in new window or tab >>System Identification of Wiener Systems via Volterra-Laguerre Models: Application to Human Smooth Pursuit Analysis
2015 (English)In: 2015 European Control Conference (Ecc), IEEE , 2015, p. 2700-2705Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents novel means for estimating the polynomial static nonlinearity coefficients of a Wiener system in absence of a priori information about the linear block. To capture the system structure, the identification is performed with respect to a Volterra series model, whose kernels are parameterized in terms of Laguerre functions. A property of the resulting Volterra-Laguerre model is exploited to allow for straightforward identification of the coefficients of the output polynomial. The proposed method is shown to provide accurate estimates of the polynomial coefficients even for noisy data. Finally, the method is applied to eye-tracking data obtained to characterize the human smooth pursuit system. The resulting models are evaluated in terms of prediction accuracy and shown to outperform models of previous research.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-427603 (URN)10.1109/ECC.2015.7330946 (DOI)000380485400430 ()
Conference
2015 EUROPEAN CONTROL CONFERENCE (ECC), July 15-17, Linz, Austria
Available from: 2020-12-10 Created: 2020-12-10 Last updated: 2020-12-10Bibliographically approved
Jansson, D. (2014). Mathematical modeling of the human smooth pursuit system. (Licentiate dissertation). Uppsala University
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
Jansson, D. & Medvedev, A. (2014). Volterra modeling of the smooth pursuit system with application to motor symptoms characterization in Parkinson's disease. In: 2014 European Control Conference (ECC): . Paper presented at 13th European Control Conference (ECC), JUN 24-27, 2014, Strasbourg, France (pp. 1856-1861). IEEE
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, p. 1856-1861Conference 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
Jansson, D., Rosén, O. & Medvedev, A. (2013). Non-parametric analysis of eye-tracking data by anomaly detection. In: Proc. 12th European Control Conference: . Paper presented at ECC 2013, July 17-19, Zürich, Switzerland (pp. 632-637). IEEE
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, p. 632-637Conference 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
Rosén, O., Medvedev, A. & Jansson, D. (2013). Non-parametric anomaly detection in trajectorial data.
Open this publication in new window or tab >>Non-parametric anomaly detection in trajectorial data
2013 (English)Manuscript (preprint) (Other academic)
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-229019 (URN)
Available from: 2013-04-19 Created: 2014-07-25 Last updated: 2014-07-25Bibliographically approved
Jansson, D. & Medvedev, A. (2013). Parametric and non-parametric stochastic anomaly detection in analysis of eye-tracking data. In: Proc. 52nd Conference on Decision and Control: . Paper presented at CDC 2013, December 10-13, Florence, Italy (pp. 2532-2537). Piscataway, NJ: IEEE
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, p. 2532-2537Conference 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
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