The Cognitive Behavioral Assistive Technology (CB-AT) Team focuses on developing intervention technologies for maintaining the cognitive health of older adults by improving the quality of their social interactions towards the prevention of dementia. A conversation support method named the Coimagination Method has been employed in key technologies to realize such social interactions.

Now that the prevalence of dementia has become a social problem, there is a great and growing need for developing human-assistive artificial intelligences (AIs) that can nurture and maintain cognitive health and intelligence. Indeed, one of the greatest health and social care challenges in this century is dementia, which occurs mainly in people 65 years and older. The number of people with dementia is rising rapidly, particularly because of aging populations in low and middle-income countries. One of the risk factors for dementia is social isolation. To target this risk factor, the Coimagination Method was developed in combination with assistive technologies and AI. It is am interactive, photo-integrated, conversation-based approach to cognitive resilience that utilizes the cognitive functions that begin to decline at the mild cognitive impairment level. It was proposed by the team leader, has been developed by public grants supporting innovative research, and has been applied to and accepted by older adults in diverse settings through action research.

Towards developing and evaluating the Coimagination Method and systems, three lines of research are currently underway:

System: Conversation Support Systems for Cognitive Health

The purpose of this research topic is to identify, through developing various proof-of-concept conversation support systems, what technologies and platforms can best provide a sustainable service for large-scale social implementation. A combination of action research and lab-based experiments have supplemented the development and evaluation of these systems. In particular, commercial-level prototypes of conversation support robots for moderating group conversations have been developed through commissioned manufacturing by a robotic startup company and are currently being evaluated using the analysis methods below.

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Analysis: Methods and Measures for Estimating Cognitive Functions from Physiological and Behavioral Data

The purpose of this research topic is to propose technologies for early detection and evaluation of cognitive decline and dementia using multimodal datasets that can be collected at home or in hospital settings. Physiological data include EEG and MRI while behavioral data include transcribed conversational data and activity data. An analysis method for discriminating different cognitive levels by combining tensor-network and deep learning was proposed and applied to EEG data. This result was achieved in collaboration with the Tensor Learning Unit at RIKEN AIP. Additionally, linguistic characteristics that are associated with cognitive function were identified through the analysis of conversational data.

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Evaluation: Capturing the Effects of the Systems on Cognitive Health

The purpose of this research topic is to collect evidence of the effectiveness of the intervention system and Coimagination Method. The world’s first randomized-controlled trial (RCT) of a cognitive intervention through group conversation with healthy older adults was realized in our lab. Through this, we have identified cognitive sub-functions and linguistic characteristics that can be improved by the intervention through applying the analysis methods and measures described above. This result was achieved in collaboration with medical doctors from Osaka and Keio Universities.

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