Alzahrani, F., Mirheidari, B., Blackburn, D. orcid.org/0000-0001-8886-1283 et al. (2 more authors) (2021) Eye blink rate based detection of cognitive impairment using in-the-wild data. In: 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). 9th International Conference on Affective Computing & Intelligent Interaction (ACII 2021), 28 Sep - 01 Oct 2021, Nara, Japan (virtual conference). Institute of Electrical and Electronics Engineers ISBN 9781665400206
Abstract
Investigating automatic methods for the early detection of dementia and related conditions that cause cognitive impairment is an area of growing interest. Video processing could play a role by providing a non-invasive and low-cost alternative to current expensive assessments. For this to be successful it is crucial that approaches are robust to in-the-wild challenges. In this paper, visual cues, related to the eye blink rate (EBR), are investigated to quantify the early phase of neurodegenerative disorder (ND) and mild cognitive impairment (MCI) as well as functional memory disorder (FMD; problems with memory not related to neurodegenerative disorder). This paper aims to improve the detection of ND and MCI by investigating a novel approach to calculating the EBR that is more robust to in-the-wild challenges. An in-house dataset with 18 participants is used. The EBR is calculated from eye landmarks extracted using two libraries (Dlib and Openface). To mitigate issues observed in the noisy, in-the-wild recordings, a multiple threshold approach for EBR detection is proposed. It involves generating multiple thresholds for identifying a blink, where a threshold is used to determine whether an eye is open or closed. Several supervised machine learning approaches are used for automatic classification. The results show that accuracy measures of 89% and 78% are achieved using Dlib and OpenFace data, respectively, when distinguishing between three conditions with ND, MCI and FMD.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | cognitive impairment; dementia; eye blink rate |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Aug 2022 13:30 |
Last Modified: | 15 Nov 2022 01:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/acii52823.2021.9597424 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190009 |