{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T08:07:26Z","timestamp":1791274046310,"version":"4.1.0"},"reference-count":48,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":87,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp;amp; Dem Diag Ass &amp;amp; Dis Mo"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>INTRODUCTION<\/jats:title><jats:p>To detect early cognitive impairment in community\u2010dwelling older adults, this study explored the viability of artificial intelligence (AI)\u2010assisted linear acceleration and angular velocity analysis during walking.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>This cross\u2010sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini\u2010Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The models\u2019 average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>DISCUSSION<\/jats:title><jats:p>AI\u2010enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention.<\/jats:p><\/jats:sec><jats:sec><jats:title>Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Artificial intelligence (AI)\u2010enabled gait analysis can be used to detect the early signs of cognitive decline.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>This AI model was constructed using data from a community\u2010dwelling cohort.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>AI\u2010assisted linear acceleration and angular velocity analysis during gait was used.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>The model may help in early detection of dementia.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/dad2.70012","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T03:55:07Z","timestamp":1727322907000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial intelligence detection of cognitive impairment in older adults during walking"],"prefix":"10.1002","volume":"16","author":[{"given":"Shuichi P.","family":"Obuchi","sequence":"first","affiliation":[{"name":"Research Team for Human Care Tokyo Metropolitan Institute for Geriatrics and Gerontology  Itabashi\u2010ku Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Motonaga","family":"Kojima","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy University of Tokyo Health Sciences  Tama\u2010shi Tokyo 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