Automatic detection of very early stage of dementia through multimodal interaction with computer avatars

This paper proposes a new approach to detecting very early stage of dementia automatically. We develop a computer avatar with spoken dialog functionalities that produces natural spoken queries referring to Mini Mental State Examination, Wechsler Memory Scale-Revised and other related questions. Multimodal interactive data of spoken dialogues from 18 participants (9 dementias and 9 healthy controls) are recorded, and audiovisual features are extracted. We confirm that the support vector machines can classify into two groups with 0.94 detection performance as measured by areas under ROC curve. It is found that our system has possibilities to detect very early stage of dementia through spoken dialog with our computer avatars.