Abstract
In recent years, the number of patients with dementia has steadily increased. It is possible to recover from Mild Cognitive Impairment (MCI), which is a preliminary stage of dementia. Therefore, it is important to detect the signs of MCI at home as some characteristics of MCI will appear in daily life. In this study, we defined some features that can detect MCI in daily life when at home by focusing on daily conversations. The acoustic and linguistic features in conversation that were related to cognitive function were selected for analysis. In our experiment, features that were effective in detecting MCI were extracted using the corpus of conversations between healthy controls (HC) and elderly people with dementia, which includes MCI. Based on our findings, effective features to detect MCI were identified. We also used two different machine learning models to discriminate between the two groups using the features effective for MCI detection. We obtained more than 80% correct answers in both cases. In this report, we confirmed the elemental screening method for the TV-based screening system for MCI.
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Yamanaka, A., Sato, I., Matsumoto, S., Fujino, Y. (2023). Mild Cognitive Impairment Screening System by Multiple Daily Activity Information—A Method Based on Daily Conversation. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-99-3091-3_28
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DOI: https://doi.org/10.1007/978-981-99-3091-3_28
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