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.
@article{arxiv.1709.01613,
title = {Machine Learning and Social Robotics for Detecting Early Signs of Dementia},
author = {Patrik Jonell and Joseph Mendelson and Thomas Storskog and Goran Hagman and Per Ostberg and Iolanda Leite and Taras Kucherenko and Olga Mikheeva and Ulrika Akenine and Vesna Jelic and Alina Solomon and Jonas Beskow and Joakim Gustafson and Miia Kivipelto and Hedvig Kjellstrom},
journal= {arXiv preprint arXiv:1709.01613},
year = {2017}
}