Authors:
Yuri Almeida
1
;
2
;
Manisha Sanjay Sirsat
1
;
Sergi Bermúdez i Badia
3
;
2
and
Eduardo Fermé
1
;
2
Affiliations:
1
NOVA-LINCS, Portugal
;
2
Universidade da Madeira, Portugal
;
3
Madeira Interactive Technologies Institute, Portugal
Keyword(s):
Long Term Care in Cognitive Neurorehabilitation, Profiling Challenges, Machine Learning, Belief Revision.
Abstract:
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 toward
s 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.
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