Home › Research › Digital health tools
Detecting Dementia Through Interactive Computer Avatars
Tanaka H, Adachi H, Ukita N, Ikeda M, Kazui H, Kudo T, Nakamura S.. IEEE journal of translational engineering in health and medicine, 2017-09-15
Topic: Risk, prevention and care in population studies
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.
Key findings
- 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.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Participants29
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 28 of the 58 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE journal of translational engineering in health and medicine) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2017 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 29 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 29018636) |
| Free to read | 2 / 2 | Free to read |
| Points | 28 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 28 of the 58 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Tanaka, H., Adachi, H., Ukita, N., Ikeda, M., Kazui, H., Kudo, T., et al. (2017). Detecting Dementia Through Interactive Computer Avatars. IEEE journal of translational engineering in health and medicine. https://doi.org/10.1109/jtehm.2017.2752152
Vancouver
Tanaka H, Adachi H, Ukita N, Ikeda M, Kazui H, Kudo T, et al. Detecting Dementia Through Interactive Computer Avatars. IEEE journal of translational engineering in health and medicine. 2017. doi:10.1109/jtehm.2017.2752152. PMID: 29018636.
BibTeX
@article{tanaka2017-29018636,
author = {Tanaka, H. and Adachi, H. and Ukita, N. and Ikeda, M. and Kazui, H. and Kudo, T. and Nakamura, S.},
title = {{Detecting Dementia Through Interactive Computer Avatars}},
journal = {IEEE journal of translational engineering in health and medicine},
year = {2017},
doi = {10.1109/jtehm.2017.2752152},
pmid = {29018636},
url = {https://doi.org/10.1109/jtehm.2017.2752152}
}