Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3123024.3125613acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

Towards early dementia detection by oculomotor performance analysis on leisure web content

Published: 11 September 2017 Publication History

Abstract

The oculomotor performance can be an indicator of early neurodegeneration. Persons with Alzheimer's disease have shown different eye movement patterns than healthy persons in visual exploration through different experiments. We present a non-obtrusive approach for the assessment of oculomotor performance that can be useful to detect early stages of dementia. Our proposal uses a non-intrusive method to analyze how people explore leisure web content. In this paper, we discuss literature, our initial design, and the future directions of the project.

References

[1]
Marina Von Zuben A and Ivan Aprahamian. 2014. Eye movement analysis and cognitive processing: detecting indicators of conversion to Alzheimer's disease. Neuropsychiatric disease and treatment 1273--1285.
[2]
Sarah A Chau, Nathan Herrmann, Chelsea Sherman, and Jonathan Chung. 2017. Visual Selective Attention Toward Novel Stimuli Predicts Cognitive Decline in Alzheimer's Disease Patients. Journal of Alzheimer's Disease. 1--11.
[3]
Michael D Crutcher, Rose Calhoun-Haney, Cecelia M Manzanares, et al. 2009. Eye tracking during a visual paired comparison task as a predictor of early dementia. Am J Alzheimers Dis Other Demen 24, 3: 258--266.
[4]
Gerardo Fern. 2016. Patients with Mild Alzheimer's Disease Fail When Using Their Working Memory: Evidence from the Eye Tracking Technique. Journal of Alzheimer's Disease 50: 827--838.
[5]
Quentin Lenoble, Giovanna Bubbico, Sébastien Szaffarczyk, Florence Pasquier, and Muriel Boucart. 2015. Scene categorization in Alzheimer's disease: A saccadic choice task. Dementia and Geriatric Cognitive Disorders Extra 5, 1: 1--12.
[6]
Alexandra Papoutsaki, Patsorn Sangkloy, James Laskey, Nediyana Daskalova, Jeff Huang, and James Hays. 2016. WebGazer: Scalable Webcam Eye Tracking Using User Interactions. Ijcai: 3839--3845.
[7]
Boucart, M., Bubbico, G., Szaffarczyk, S., & Pasquier, F. 2013. Animal Spotting in Alzheimer's Disease: an eye tracking study of object categorization. Journal of Alzheimer's Disease, 39(1), 181--189.
[8]
Nicolas Riche, Matthieu Duvinage, Matei Mancas, Bernard Gosselin, and Thierry Dutoit. 2013. Saliency and human fixations: State-of-the-art and study of comparison metrics. Proceedings of the IEEE International Conference on Computer Vision: 1153--1160.
[9]
Sarah C Seligman and Tania Giovannetti. 2015. The Potential Utility of Eye Movements in the Detection and Characterization of Everyday Functional Difficulties in Mild Cognitive Impairment. Neuropsychology review. 25. 199--215.
[10]
Andrea Tales and Gillian Porter. 2008. Visual attention-related processing in Alzheimer's disease. Reviews in Clinical Gerontology 18, 3: 229.
[11]
Coubard, O. A. 2016. What do we know about eye movements in Alzheimer's disease? The past 37 years and future directions. Future Medicine. 677--680.
[12]
Vanessa Vallejo, Dario Cazzoli, Luca Rampa, et al. 2016. Effects of Alzheimer's Disease on Visual Target Detection: A "Peripheral Bias." Frontiers in aging neuroscience 8.
[13]
Kang Wang and Shen Wang. 2016. Deep eye fixation map learning for calibration-free eye gaze tracking. ACM Symposium on Eye Tracking Research & Applications. 47--55.
[14]
Bum-soo Yoo and Jong-hwan Kim. 2013. Scanpaths Analysis with Fixation Maps to Provide Factors for Natural Gaze Control. In Robot Intelligence Technology and Applications 2. 361--368.
[15]
Alan Yung. 2016. Impact of Facebook Usage on Macau's People Aged 45 and Above: Implications for Marketers, Social Workers and Policy Makers. Jaipuria International Journal of Management Research 2, 1.
[16]
Tobii. Retrieved April 1, 2017 from http://www.tobii.com/.
[17]
SMI Eye Tracking Glasses. Retrieved April 1, 2017 from https://www.smivision.com/eye-tracking/product/eye-tracking-glasses/.
[18]
Neurotrack. Retrieved June 8, 2017 from www.neurotrack.com.

Cited By

View all
  • (2022)STEP-UP: Enabling Low-Cost IMU Sensors to Predict the Type of Dementia During Everyday Stair ClimbingFrontiers in Computer Science10.3389/fcomp.2021.8049173Online publication date: 31-Jan-2022
  • (2022)CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry2022 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom53586.2022.9762401(66-76)Online publication date: 21-Mar-2022
  • (2020)Mental Health and SensingSignal Processing Techniques for Computational Health Informatics10.1007/978-3-030-54932-9_11(247-260)Online publication date: 8-Oct-2020
  • Show More Cited By

Index Terms

  1. Towards early dementia detection by oculomotor performance analysis on leisure web content

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
    September 2017
    1089 pages
    ISBN:9781450351904
    DOI:10.1145/3123024
    © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 September 2017

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. alzheimer' disease
    2. early diagnosis
    3. eye movements
    4. oculomotor performance

    Qualifiers

    • Research-article

    Conference

    UbiComp '17

    Acceptance Rates

    Overall Acceptance Rate 764 of 2,912 submissions, 26%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Nov 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)STEP-UP: Enabling Low-Cost IMU Sensors to Predict the Type of Dementia During Everyday Stair ClimbingFrontiers in Computer Science10.3389/fcomp.2021.8049173Online publication date: 31-Jan-2022
    • (2022)CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry2022 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom53586.2022.9762401(66-76)Online publication date: 21-Mar-2022
    • (2020)Mental Health and SensingSignal Processing Techniques for Computational Health Informatics10.1007/978-3-030-54932-9_11(247-260)Online publication date: 8-Oct-2020
    • (2018)A preliminary study using a web camera based eye tracking to assess novelty reaction allowing user interactionProceedings of the 7th Mexican Conference on Human-Computer Interaction10.1145/3293578.3293598(1-4)Online publication date: 29-Oct-2018
    • (2018)Sensing Technologies for Monitoring Serious Mental IllnessesIEEE MultiMedia10.1109/MMUL.2018.01192123625:1(61-75)Online publication date: Jan-2018

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media