The dual-task paradigm is a promising procedure for estimating cognitive status and mayalso be collaterally used to reduce cognitive decline and prevent dementia. In this paper, we use the minimental state exam (MMSE) to the assess cognitive status in the elderly as a reference and investigate thepotential of using machine learning for early detecting cognitive impairment in the elderly. Although manystudies have suggested that dual-task performance, in which participants perform a cognitive task whilewalking, is associated with cognition, they only considered the correlation between cognitive parametersand simple gait feature, such as gait speed, through the statistical analysis. We instead use a Kinect sensorto capture participants’ whole-body movements and extract a rich gait feature that has the ability to exhibitdifferent tendencies of movements between healthy and cognitive-impaired elderlies. In our experiments,a classifier based on the dual-task gait feature achieved a higher performance than the one based on thesingle-task feature; the performance of the rich gait feature was better than that of a simple one, and; anoptimal detection performance was achieved with an MMSE cutoff score of 25. We positively validated thatthe proposed method could early detect elderly with lower MMSE scores based on dual-task gait feature witha promising performance. Our approach can support early and automated diagnosis of cognitive impairment.
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