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- AI-Rehab: a framework for AI driven neurorehabilitation training - the profiling challengePublication . Fermé, Eduardo; Bermúdez i Badia, Sergi; Sirsat, Manisha; Almeida, YuriOne 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.
- Knowledge-driven profile dynamicsPublication . Fermé, Eduardo; Garapa, Marco; Reis, Maurício D. L.; Almeida, Yuri; Paulino, Teresa; Mariana Rodrigues; Garapa, Marco; Aristides da Silva Godinho de Almeida, Yuri; Fermé, Eduardo; Reis, Maurício; Paulino, TeresaIn the last decades, user profiles have been used in several areas of information technology. In the literature, most research works, and systems focus on the creation of profiles (using Data Mining techniques based on user’s navigation or interaction history). In general, the dynamics of profiles are made by means of a systematic recreation of the profiles, without using the previous profiles. In this paper we propose to formalize the creation, representation, and dynamics of profiles from a Knowledge-Driven perspective. We introduce and axiomatically characterize four operators for changing profiles using a belief change inspired approach.