{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:06:04Z","timestamp":1779062764100,"version":"3.51.4"},"posted":{"date-parts":[[2025,5,25]]},"group-title":"Health Informatics","reference-count":74,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,17]]},"abstract":"<jats:p>Behavioral sensing using wearables has emerged as a valuable tool for screening of neurodegenerative conditions, including Mild Cognitive Impairment (MCI). Existing work has primarily focused on using wearables to quantify walking patterns in individuals with MCI, typically in controlled environments.\n  On the other hand, the human activity recognition community has been actively studying methods to quantify kitchen activities, which are instrumental activities of daily living (IADLs).\n  Previous studies have reported deficits in visuospatial navigation among individuals with MCI, which may affect functional independence in the kitchen.\n  This work studies the use of wrist and eye-tracking wearable sensors to quantify kitchen activities in individuals with MCI. We collected multimodal datasets from 18 older adults (11 with MCI and 7 with normal cognition) while preparing a yogurt bowl.\n  Our multimodal analysis model could classify the older adults with MCI and those with normal cognition with a 76\\% F1 score.\n  The feature importance analysis showed an association between weaker upper-limb motor function and delayed eye movements and cognitive decline, consistent with previous findings in MCI research from lab studies.\n  This pilot study demonstrates the feasibility of monitoring behavior markers of MCI in daily living settings and calls for further studies with larger-scale validation in individuals' home environments.<\/jats:p>","DOI":"10.1101\/2025.05.24.25328107","type":"posted-content","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T14:20:11Z","timestamp":1748182811000},"source":"Crossref","is-referenced-by-count":0,"title":["Feasibility of Quantifying Mild Cognitive Impairment Using Multi-Modal Wearable Sensors in a Kitchen Environment"],"prefix":"10.64898","author":[{"given":"Bonwoo","family":"Koo","sequence":"first","affiliation":[{"name":"Korea Advanced Institute of Science and Technology;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Bilau","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amy D.","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Emory University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eunhwa","family":"Yang","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-3278","authenticated-orcid":false,"given":"Hyeokhyen","family":"Kwon","sequence":"additional","affiliation":[{"name":"Emory University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025052709151964000_2025.05.24.25328107v1.1","first-page":"pafac173","article-title":"Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies","volume":"51","year":"2022","journal-title":"Age and ageing"},{"key":"2025052709151964000_2025.05.24.25328107v1.2","doi-asserted-by":"crossref","first-page":"1423790","DOI":"10.3389\/fdgth.2024.1423790","article-title":"Advancements in eye movement measurement technologies for assessing neurodegenerative diseases","volume":"6","year":"2024","journal-title":"Frontiers in Digital Health"},{"key":"2025052709151964000_2025.05.24.25328107v1.3","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window size impact in human activity recognition","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"2025052709151964000_2025.05.24.25328107v1.4","doi-asserted-by":"crossref","unstructured":"Barbara Bruno , Fulvio Mastrogiovanni , and Antonio Sgorbissa . 2014. 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