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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorKim, Yuwon-
dc.contributor.authorPark, Jinseok-
dc.contributor.authorChoi, Hojin-
dc.contributor.authorLoeser, Martin-
dc.contributor.authorRyu, Hokyoung-
dc.contributor.authorSeo, Kyoungwon-
dc.date.accessioned2024-05-17T12:48:14Z-
dc.date.available2024-05-17T12:48:14Z-
dc.date.issued2024-
dc.identifier.isbn979-8-4007-0331-7/24/05de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30688-
dc.description.abstractThe imperative for early mild cognitive impairment (MCI) detection is underscored by the limitations of traditional biomarkers, high cost and invasiveness, and they often fail to capture behavioral changes in MCI patients associated with impaired instrumental activities of daily living (IADL). This study introduces a cost-effective, non-invasive alternative using digital markers, “virtual kiosk test”, which involves performing IADL tasks such as ordering food via a kiosk in virtual reality (VR) to detect MCI at an early stage. Involving 20 healthy controls and 31 MCI patients, four key behavioral features within VR digital markers effectively differentiate groups: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. A machine learning model demonstrated high effectiveness with 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and a 94.7% F1-score in group differentiation. Findings suggest that observing behaviors via the virtual kiosk test within 5 minutes can be an efficient approach for early MCI detection, acting as reliable VR digital markers.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectMachine learningde_CH
dc.subjectDigital healthde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc616.8: Neurologie und Krankheiten des Nervensystemsde_CH
dc.titleDecoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairmentde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
dc.identifier.doi10.1145/3613905.3650731de_CH
zhaw.conference.detailsConference on Human Factors in Computing Systems (CHI), Honolulu, USA, 11-16 May 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.title.proceedingsCHI EA '24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Kim, Y., Park, J., Choi, H., Loeser, M., Ryu, H., & Seo, K. (2024). Decoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairment. CHI EA ’24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613905.3650731
Kim, Y. et al. (2024) ‘Decoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairment’, in CHI EA ’24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. Available at: https://doi.org/10.1145/3613905.3650731.
Y. Kim, J. Park, H. Choi, M. Loeser, H. Ryu, and K. Seo, “Decoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairment,” in CHI EA ’24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, 2024. doi: 10.1145/3613905.3650731.
KIM, Yuwon, Jinseok PARK, Hojin CHOI, Martin LOESER, Hokyoung RYU und Kyoungwon SEO, 2024. Decoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairment. In: CHI EA ’24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. Conference paper. Association for Computing Machinery. 2024. ISBN 979-8-4007-0331-7/24/05
Kim, Yuwon, Jinseok Park, Hojin Choi, Martin Loeser, Hokyoung Ryu, and Kyoungwon Seo. 2024. “Decoding Behavior : Utilizing Virtual Reality Digital Marker and Machine Learning for Early Detection of Mild Cognitive Impairment.” Conference paper. In CHI EA ’24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. https://doi.org/10.1145/3613905.3650731.
Kim, Yuwon, et al. “Decoding Behavior : Utilizing Virtual Reality Digital Marker and Machine Learning for Early Detection of Mild Cognitive Impairment.” CHI EA ’24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, 2024, https://doi.org/10.1145/3613905.3650731.


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