Cited by 127Dementia and Cognitive Impairment ResearchEmotion and Mood RecognitionSpeech and dialogue systems

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Detecting Dementia Through Interactive Computer Avatars

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Tanaka, Hiroki, Adachi, Hiroyoshi, Ukita, Norimichi, Ikeda, Manabu, Kazui, Hiroaki, Kudo, Takashi, Nakamura, Satoshi·2017·IEEE Journal of Translational Engineering in Health and Medicine

DOI 10.1109/jtehm.2017.2752152PMC PMC5630006

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Abstract

This paper proposes a new approach to automatically detect dementia. Even though some works have detected dementia from speech and language attributes, most have applied detection using picture descriptions, narratives, and cognitive tasks. In this paper, we propose a new computer avatar with spoken dialog functionalities that produces spoken queries based on the mini-mental state examination, the Wechsler memory scale-revised, and other related neuropsychological questions. We recorded the interactive data of spoken dialogues from 29 participants (14 dementia and 15 healthy controls) and extracted various audiovisual features. We tried to predict dementia using audiovisual features and two machine learning algorithms (support vector machines and logistic regression). Here, we show that the support vector machines outperformed logistic regression, and by using the extracted features they classified the participants into two groups with 0.93 detection performance, as measured by the areas under the receiver operating characteristic curve. We also newly identified some contributing features, e.g., gap before speaking, the variations of fundamental frequency, voice quality, and the ratio of smiling. We concluded that our system has the potential to detect dementia through spoken dialog systems and that the system can assist health care workers. In addition, these findings could help medical personnel detect signs of dementia.

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Authors: Tanaka, H., Adachi, H., Ukita, N., Ikeda, M., Kazui, H., Kudo, T., & Nakamura, S.
Affiliations: Nara Institute of Science and Technology (JP)
The University of Osaka (JP)
Toyota Technological Institute (JP)
Corresponding author: Tanaka, Hiroki
ORCID: Tanaka, Hiroki
Adachi, Hiroyoshi
Ukita, Norimichi
Ikeda, Manabu
Kazui, Hiroaki
Kudo, Takashi
Nakamura, Satoshi
Venue: IEEE Journal of Translational Engineering in Health and Medicine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2017
Volume: 5
Pages: 1-11
ISSN: 2168-2372 (ISSN-L)
DOI: 10.1109/jtehm.2017.2752152
PMC: PMC5630006
OpenAlex: W2755232679
Cited by: 127
References: 64
Citations by year: 2026: 10 · 2025: 8 · 2024: 9 · 2023: 19 · 2022: 18 · 2021: 19 · 2020: 18 · 2019: 18 · 2018: 6 · 2017: 2
FWCI: 9.89
Type: journal article
Language: en
Open access: gold
doi.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Journal of Translational Engineering in Health and Medicine (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: JSPS KAKENHI (JP17H06101, JP16K16172)
Yanmar Innovation Lab. 2112
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords: dementia detection, spoken dialogue systems, audio-visual features, fundamental frequency, voice quality, smiling, Mini-Mental State Examination, Wechsler Memory Scale-Revised, neuropsychological assessment, support vector machine, logistic regression, speech analysis

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Detecting Dementia Through Interactive Computer Avatars

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