Fuzzy adaptive cognitive stimulation therapy generation for Alzheimer’s sufferers: Towards a pervasive dementia care monitoring platform

Javier Navarri, Faiyaz Doctor, Víctor Zamudio, Rahat Iqbal, Arun K Sangaiah, Carlos Lino

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)
    36 Downloads (Pure)

    Abstract

    In this paper, we present a novel system for cognitive stimulation therapy to progressively assess cognitive impairment and emotional well-being of dementia patients in social care settings. The system assesses patients interactions and computes performance scores for different areas of cognitive stimulation. Patient interactions are initially classified into predefined performance categories through clustering of a sampled population. New personalized stimulation plans tailored to match the patient’s changing level of impairment are generated automatically through a set of fuzzy rule based systems using quantitative attributes and the overall scores of patients interactions. Therapists can redefine, evaluate and adjust the rules governing difficulty and activity levels for different stimulation areas to fine tune generated activity plans. The system can also be combined with an Internet of Things (IoT) enabled patient dialogue system for determining the affective state of participants during therapy sessions that could be used as a pervasive condition monitoring platform. Experiments consisting of therapy sessions of patients interacting with the system were performed in which the activity plans were automatically generated. Initial results showed that the system outputs were in agreement with the therapists own assessment in most of the stimulation areas. Simulation experiments were also conducted to analyse the system performance over multiple sessions. The results suggest that the system is able to adapt therapy plans overtime in response to changing levels of impairment/performance while supporting therapists to tune and evaluate therapy plans more effectively.
    Original languageEnglish
    Pages (from-to)479-490
    Number of pages12
    JournalFuture Generation Computer Systems
    Volume88
    Early online date19 Jun 2018
    DOIs
    Publication statusPublished - Nov 2018

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems [88], (2018)] DOI: 10.1016/j.future.2018.06.01

    © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

    Keywords

    • Fuzzy system
    • Computer assisted therapy
    • Cognitive impairment
    • Clustering

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