Qualitative and Quantitative Scoring and Evaluation of the Eye Movement Classification Algorithms

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dc.contributor.authorKomogortsev, Oleg
dc.contributor.authorJayarathna, Sampath
dc.contributor.authorKoh, Do Hyong
dc.contributor.authorGowda, Sandeep Munikrishne
dc.date.accessioned2009-11-23T10:03:26Z
dc.date.available2012-02-24T10:03:26Z
dc.date.issued2009-09-16
dc.description.abstractThis paper presents a set of qualitative and quantitative scores designed to assess performance of the various eye movement classification algorithms. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (I-HMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the \"best\".
dc.description.departmentComputer Science
dc.formatText
dc.format.extent10 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationKomogortsev, O. V., Jayarathna, S., Koh, D. H., & Gowda, S. M. (2009). Qualitative and quantitative scoring and evaluation of the eye movement classification algorithm (Report No. TXSTATE-CS-TR-2009-16). Texas State University-San Marcos, Department of Computer Science.
dc.identifier.urihttps://hdl.handle.net/10877/2577
dc.language.isoen
dc.subjecteye movements
dc.subjectclassification
dc.subjectalgorithm
dc.subjectanalysis
dc.subjectscoring
dc.subjectmetrics
dc.subjectComputer Science
dc.titleQualitative and Quantitative Scoring and Evaluation of the Eye Movement Classification Algorithms
dc.typeTechnical Report

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