Detecting Dementia Through Interactive Computer Avatars
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8038769/ |
| _version_ | 1818417338318323712 |
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| author | Hiroki Tanaka Hiroyoshi Adachi Norimichi Ukita Manabu Ikeda Hiroaki Kazui Takashi Kudo Satoshi Nakamura |
| author_facet | Hiroki Tanaka Hiroyoshi Adachi Norimichi Ukita Manabu Ikeda Hiroaki Kazui Takashi Kudo Satoshi Nakamura |
| author_sort | Hiroki Tanaka |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T12:05:12Z |
| format | Article |
| id | doaj.art-fd55d49aa34e45109a0ba9145d614722 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-12-14T12:05:12Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-fd55d49aa34e45109a0ba9145d6147222022-12-21T23:01:54ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722017-01-01511110.1109/JTEHM.2017.27521528038769Detecting Dementia Through Interactive Computer AvatarsHiroki Tanaka0https://orcid.org/0000-0002-0548-6252Hiroyoshi Adachi1Norimichi Ukita2Manabu Ikeda3Hiroaki Kazui4Takashi Kudo5Satoshi Nakamura6Graduate School of Information Science, Nara Institute of Science and Technology, Nara, JapanHealth and Counseling Center, Osaka University, Osaka, JapanGraduate School of Engineering, Toyota Technological Institute, Nagoya, JapanDepartment of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, JapanDepartment of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, JapanHealth and Counseling Center, Osaka University, Osaka, JapanGraduate School of Information Science, Nara Institute of Science and Technology, Nara, JapanThis 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.https://ieeexplore.ieee.org/document/8038769/Dementiaspoken dialoguecomputer avatarsAlzheimer’s diseaseMMSE |
| spellingShingle | Hiroki Tanaka Hiroyoshi Adachi Norimichi Ukita Manabu Ikeda Hiroaki Kazui Takashi Kudo Satoshi Nakamura Detecting Dementia Through Interactive Computer Avatars IEEE Journal of Translational Engineering in Health and Medicine Dementia spoken dialogue computer avatars Alzheimer’s disease MMSE |
| title | Detecting Dementia Through Interactive Computer Avatars |
| title_full | Detecting Dementia Through Interactive Computer Avatars |
| title_fullStr | Detecting Dementia Through Interactive Computer Avatars |
| title_full_unstemmed | Detecting Dementia Through Interactive Computer Avatars |
| title_short | Detecting Dementia Through Interactive Computer Avatars |
| title_sort | detecting dementia through interactive computer avatars |
| topic | Dementia spoken dialogue computer avatars Alzheimer’s disease MMSE |
| url | https://ieeexplore.ieee.org/document/8038769/ |
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