Machine Learning and Social Robotics for Detecting Early Signs of Dementia. Jonell, P., Mendelson, J., Storskog, T., Hagman, G., Ostberg, P., Leite, I., Kucherenko, T., Mikheeva, O., Akenine, U., Jelic, V., Solomon, A., Beskow, J., Gustafson, J., Kivipelto, M., & Kjellstrom, H. 2017.
Paper abstract bibtex 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.
@misc{
title = {Machine Learning and Social Robotics for Detecting Early Signs of Dementia},
type = {misc},
year = {2017},
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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.},
bibtype = {misc},
author = {Jonell, Patrik and Mendelson, Joseph and Storskog, Thomas and Hagman, Goran and Ostberg, Per and Leite, Iolanda and Kucherenko, Taras and Mikheeva, Olga and Akenine, Ulrika and Jelic, Vesna and Solomon, Alina and Beskow, Jonas and Gustafson, Joakim and Kivipelto, Miia and Kjellstrom, Hedvig}
}
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