Machine Learning and Social Robotics for Detecting Early Signs of Dementia

TL;DR

This paper introduces the EACare project, which combines machine learning and social robotics to develop an embodied system capable of early dementia detection through neuropsychological tests, using participatory design and initial prototypes.

Contribution

It presents a novel interdisciplinary approach integrating social robotics and machine learning for early dementia detection, including initial prototype development.

Findings

Development of a Wizard of Oz prototype

Use of participatory design methods

Framework for training with clinician-patient interactions

Abstract

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.