Machine Learning and Social Robotics for Detecting Early Signs of Dementia
This addresses the problem of early dementia detection for patients and clinicians, but appears incremental as it builds on existing methods in a new application.
The EACare project aims to develop an embodied system using machine learning and social robotics to detect early signs of dementia by conducting neuropsychological tests, and reports on a first Wizard of Oz prototype.
This paper presents the EACare project, an ambitious multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.