Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCST.2014.2364958 | Control Systems Technology, IEEE Transactions |
Keywords | Field | DocType |
Trajectory,Estimation,Visualization,Data models,Approximation methods,Monitoring,Vectors | Smooth pursuit,Data modeling,Anomaly detection,Computer vision,Experimental data,Computer science,Nonparametric statistics,Eye tracking,Parametric statistics,Orthonormal basis,Artificial intelligence | Journal |
Volume | Issue | ISSN |
PP | 99 | 1063-6536 |
Citations | PageRank | References |
2 | 0.41 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Jansson | 1 | 17 | 3.32 |
| Olov Rosen | 2 | 23 | 4.10 |
| Alexander Medvedev | 3 | 72 | 22.43 |