Detecting Dementia Through Interactive Computer Avatars
Open accessAbstract
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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Voice-based interaction opens the door to voice or speech analysis, whereas features of the patient's utterances can be passively monitored to derive health states.
“Third, it opens the door to voice or speech analysis, whereas features of the patient’s utterances can be passively monitored to derive health states [28-31].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [31]
Only four studies (2%) actually bring remote use into practice by experimenting with multi-modal human-robot interaction, infrastructure-free, or telephone-based approaches.
“Only four studies (2%), actually bring this into practice by experimenting with multi-modal human-robot interaction [94], infrastructure-free [77], or telephone-based [96, 97] approaches.”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [94]
Another four studies take into account feasibility for clinical screening within their system design.
“Another four studies take into account feasibility for clinical screening within their system design, for example, collecting data directly with a computerized decision support system [60], through human-robot interaction [94], a computer-supported screening tool [77], or simulating telephone-based data [96].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [94]
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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
- Venue
- IEEE Journal of Translational Engineering in Health and Medicine
- Published
- 2017
- Volume
- 5
- Pages
- 1-11
- ISSN
- 2168-2372 (ISSN-L)
- 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
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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