Decoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairment

Kim, Yuwon; Park, Jinseok; Choi, Hojin; Loeser, Martin; Ryu, Hokyoung; Seo, Kyoungwon

doi:10.1145/3613905.3650731

Publication type:	Conference paper
Type of review:	Peer review (abstract)
Title:	Decoding behavior : utilizing virtual reality digital marker and machine learning for early detection of mild cognitive impairment
Authors:	Kim, Yuwon Park, Jinseok Choi, Hojin Loeser, Martin Ryu, Hokyoung Seo, Kyoungwon
et. al:	No
DOI:	10.1145/3613905.3650731
Proceedings:	CHI EA '24 : Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems
Conference details:	Conference on Human Factors in Computing Systems (CHI), Honolulu, USA, 11-16 May 2024
Issue Date:	2024
Publisher / Ed. Institution:	Association for Computing Machinery
ISBN:	979-8-4007-0331-7/24/05
Language:	English
Subjects:	Artificial intelligence; Machine learning; Digital health
Subject (DDC):	006: Special computer methods 616.8: Neurology, diseases of nervous system
Abstract:	The 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30688
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Appears in collections:	Publikationen School of Engineering

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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.