Introduction: To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking.
Methods: This cross-sectional 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-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.
Results: The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets.
Discussion: AI-enabled 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.
Highlights: Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.
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| http://dx.doi.org/10.1002/dad2.70012 | DOI Listing |
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