Detecting Lower MMSE Scores in Older Adults using Cross-trial Features from a Dual-task with Gait and Arithmetic
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Wu, S., Matsuura, T., Okura, F., Makihara, Y., Zhou, C., Aoki, K., Mitsugami, I., and Yagi, Y. (2021). Detecting Lower MMSE Scores in Older Adults using Cross-trial Features from a Dual-task with Gait and Arithmetic. IEEE Access, 9:150268-150282.
The Mini-Mental State Examination (MMSE) is widely used in clinics to screen for low cognitive status. However, it is limited in that it requires examiners to be present; and has fixed questions that constrain its repeated use. Thus, the MMSE cannot be used as a daily assessment to facilitate early detection of cognitive impairment. To address this issue, we developed an automated system to detect older adults with lower MMSE scores by analyzing performance during a dual task involving stepping and calculation, which can be used repeatedly because its questions were randomly created. Leveraging this advantage, this paper proposes a learning-based method to detect subjects with lower MMSE scores using multiple trials with the dual-task system. We investigated various patterns for effectively combining the features acquired during multiple continuous trials, and analyzed the sensitivity of the number N of trials on detection performance to find the optimal N via experiments. We compared our approach with previous methods and demonstrated the superiority of our strategy. Using the cross-trial feature, our approach achieved an overall performance (sensitivity+specificity) as high as 1.79 for detecting older adults whose MMSE score is equal to or less than 23 (indicate a relatively high probability of dementia), and 1.75 for detecting older adults whose MMSE score is equal to or less than 27 (indicative of a relatively high probability of mild cognitive impairment (MCI)).
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