Machine learning and social robotics for detecting early signs of dementia

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.
abstract bibtex

Copyright © 2017, arXiv, All rights reserved. This paper presents the EACare project, an ambitious multidisciplinary 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

@misc{
 title = {Machine learning and social robotics for detecting early signs of dementia},
 type = {misc},
 year = {2017},
 source = {arXiv},
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 created = {2020-11-03T23:59:00.000Z},
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 abstract = {Copyright © 2017, arXiv, All rights reserved. This paper presents the EACare project, an ambitious multidisciplinary 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},
 bibtype = {misc},
 author = {Jonell, P. and Mendelson, J. and Storskog, T. and Hagman, G. and Ostberg, P. and Leite, I. and Kucherenko, T. and Mikheeva, O. and Akenine, U. and Jelic, V. and Solomon, A. and Beskow, J. and Gustafson, J. and Kivipelto, M. and Kjellstrom, H.}
}

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