A new method for categorizing scanpaths from eye tracking data

Haass, Michael Joseph; Matzen, Laura E.; Butler, Karin M.; Armenta, Mika

doi:10.1145/2857491.2857503

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…Oculometrics characterize the kinematics of eye movements and pupillary responses. In addition, oculometrics can be defined in relation to the stimuli (e.g., dwell time [45,46], scanpath length [47]). There are several algorithms to identify ocular events as well as various methods to define and compute oculometrics [20,48].…”

Section: Vestibulo-ocularmentioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.

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Section: Vestibulo-ocularmentioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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“…The coordinates of the eye fixation points were associated with the time-ordered eye fixation indices, which provided the sequential order of each eye fixations. By considering the overall area, data required less adjustment prior to the analysis, and the holistic visual scanning trajectories among the indicators were captured (Haass, Matzen, Butler, and Armenta, 2016). The scanning trajectory consisted of three elements: "X, Y" coordinates of eye fixations, and the timestamps of when the fixations were made.…”

Section: Discussionmentioning

confidence: 99%

Eye tracking data analytics in virtual reality training: Application in Deepwater Horizon oil drilling operation

Kang

Jeon

Salehi

2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Virtual reality (VR) enable us to train in a safe environment using computer-generated simulations. One such simulated environment is the Deepwater Horizon operation, and VR enables us to evaluate trainees’ and operators’ situation awareness (SA) in a non-hazardous environment. One unobtrusive and viable SA evaluation method might be the use of eye movements, specifically the time-ordered visual scan paths. In this research, we investigated how SA can be associated with visual scan paths in an anomaly detection task within the oil drilling rig virtual reality simulator. The results show that the trainees having lower SA tended to create random visual scan paths whereas the trainees having higher SA tended to create concentrated and refined visual scan paths. The results show promise in developing timely intervention methods through analyzing the visual scan path characteristics of the trainees.

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“…Activity, or Stimuli Inference holds publications that focus on predicting the activity or stimulus based on the assumption that different stimuli and activities produce different scanpath data. For activity prediction, the activities were often diverse, including viewing natural images, web surfing, or watching a video (Greene et al, 2012 ; Kanan et al, 2014 ; Haass et al, 2016 ; Martinez et al, 2017 ; Coutrot et al, 2018 ; Hild et al, 2018 ; Srivastava et al, 2018 ; Kucharský et al, 2020 ; Lan et al, 2020 ). However, some publications focused on specific tasks such as driving cars (Lethaus et al, 2013 ), piloting plans (Peysakhovich et al, 2022 ), or reading text (Biedert et al, 2012 ; Kelton et al, 2019 ).…”

Section: Domains and Tasksmentioning

confidence: 99%

A review of machine learning in scanpath analysis for passive gaze-based interaction

Mohamed Selim,

Barz,

Bhatti

et al. 2024

Front. Artif. Intell.

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The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.

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A new method for categorizing scanpaths from eye tracking data

Cited by 14 publications

References 12 publications

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Eye tracking data analytics in virtual reality training: Application in Deepwater Horizon oil drilling operation

A review of machine learning in scanpath analysis for passive gaze-based interaction

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