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Advisor(s)
Abstract(s)
One of the health clinic challenges is rehabilitation therapy cognitive impairment that can happen after brain
injury, dementia and in normal cognitive decline due to aging. Current cognitive rehabilitation therapy has
been shown to be the most effective way to address this problem. However, a) it is not adaptive for every
patient, b) it has a high cost, and c) it is usually implemented in clinical environments. The Task Generator
(TG) is a free tool for the generation of cognitive training tasks. However, TG is not designed to adapt and
monitor the cognitive progress of the patient. Hence, we propose in the BRaNT project an enhancement of
TG with belief revision and machine learning techniques, gamification and remote monitoring capabilities to
enable health professionals to provide a long-term personalized cognitive rehabilitation therapy at home. The
BRaNT is an interdisciplinary effort that addresses scientific limitations of current practices as well as provides
solutions towards the sustainability of health systems and contributes towards the improvement of quality of
life of patients. This paper proposes the AI-Rehab framework for the BRaNT, explains profiling challenge in
the situation of insufficient data and presents an alternate AI solutions which might be applicable once enough
data is available.
Description
Keywords
Long term care in cognitive neurorehabilitation Profiling challenges Machine learning Belief revision . Faculdade de Ciências Exatas e da Engenharia
Citation
Almeida, Y., Sirsat, M. S., i Badia, S. B., & Fermé, E. (2020). AI-Rehab: a framework for AI driven neurorehabilitation training - the profiling challenge. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: Cognitive Health IT, 845-853, 2020 , Valletta, Malta.