| Autor: |
Kenta Yoshii, Daiki Kimura, Akihiro Kosugi, Kaoru Shinkawa, Toshiro Takase, Masatomo Kobayashi, Yasunori Yamada, Miyuki Nemoto, Ryohei Watanabe, Miho Ota, Shinji Higashi, Kiyotaka Nemoto, Tetsuaki Arai, Masafumi Nishimura |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
JMIR Formative Research, Vol 7, p e42792 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2561-326X |
| DOI: |
10.2196/42792 |
| Popis: |
BackgroundThe rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. ObjectiveThis study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. MethodsThis was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. ResultsWe obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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