Oblivion Tracking: Towards a Probabilistic Working Memory Model for the Adaptation of Systems to Alzheimer Patients

Bruno Sguerra 1, 2 Pierre Jouvelot 1, 2 Samuel Benveniste 3
1 CRI - Centre de Recherche en Informatique
2 MINES ParisTech - École nationale supérieure des mines de Paris
3 CEN/STIMCO - Centre d'Expertise National en Stimulation Cognitive
Abstract : We introduce a new probabilistic working memory (WM) model that we intend to use to automatically personalize user interfaces with respect to Alzheimer patients' declining WM capacity. WM is the part of the human memory responsible for the conscious short-term storing and manipulation of information. It is known to be extremely limited and to be one of the strongest factors that impact individual diierences in cognitive abilities. In particular, individuals suuering from Alzheimer's disease have signiicantly impaired WM capacities that worsen as the disease progresses. As a use case for our model, we describe a system that is designed to help patients with Alzheimer's disease choose the music track they would like to listen to from a given playlist. We discuss how our WM model could be used to adapt this system to each patient's disease progression in time and the consequent deterioration of her WM capacity. CCS CONCEPTS • Human-centered computing →User models; • Mathematics of computing →Bayesian networks;
Keywords : Bayesian network working memory Alzheimer's disease
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Contributeur : Claire Medrala <>
Soumis le : mercredi 24 janvier 2018 - 10:42:01
Dernière modification le : lundi 12 novembre 2018 - 10:56:24

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Bruno Sguerra, Pierre Jouvelot, Samuel Benveniste. Oblivion Tracking: Towards a Probabilistic Working Memory Model for the Adaptation of Systems to Alzheimer Patients. The first International Conference on User Modeling, Adaptation and Personnalization (UMAP 2017), Jul 2017, Bratislava, Slovakia. pp.253-256 ⟨10.1145/3099023.3099052⟩. ⟨hal-01678895⟩

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