2019/04/22 by Samuel-Hunter Berndt, Berndt, Samuel-Hunter, Douglas Kirkpatrick +6 · 3 citations
Computer Science · Medicine · #Algorithm #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Eye movement #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Glaucoma and retinal disorders #Movement (music) #Pattern recognition (psychology) #Physics #Retinal Imaging and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1904.10085
published in arXiv (Cornell University) (Cornell University) · 10 pages, 18 figures, 3 tables
arxiv created 2019/04/22 · openalex publication_date 2019/04/22 · arxiv updated 2019/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The proper classification of major eye movements, saccades, fixations, and smooth pursuits, remains essential to utilizing eye-tracking data. There is difficulty in separating out smooth pursuits from the other behavior types, particularly from fixations. To this end, we propose a new offline algorithm, I-VDT-HMM, for tertiary classification of eye movements. The algorithm combines the simplicity of two foundational algorithms, I-VT and I-DT, as has been implemented in I-VDT, with the statistical predictive power of the Viterbi algorithm. We evaluate the fitness across a dataset of eight eye movement records at eight sampling rates gathered from previous research, with a comparison to the current state-of-the-art using the proposed quantitative and qualitative behavioral scores. The proposed algorithm achieves promising results in clean high sampling frequency data and with slight modifications could show similar results with lower quality data. Though, the statistical aspect of the algorithm comes at a cost of classification time.