The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
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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“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]
In the citing paper
“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]
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“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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Cited by
Sorted by citations
An overview of the features of chatbots in mental health: A scoping review
Abd-alrazaq, A. A., Alajlani, M., Alalwan, A. A., Bewick, B. M., Gardner, P., & Househ, M. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.103978 · Cited by 605
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Metadata
- 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
- 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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