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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 358638, 15 pages
Research Article

Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia

1Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI), P.O. Box 60361, 6th Charilaou-Thermi, 57001 Thessaloniki, Greece
2Department of Special Education, University of Thessaly, Argonafton & Filellinon Street, 38221 Volos, Greece
3Pan-Hellenic Federation of Alzheimer’s disease and Related Disorders, 13 P. Syndika Street, 546 43 Thessaloniki, Greece

Received 10 October 2014; Revised 23 January 2015; Accepted 4 February 2015

Academic Editor: Pietro Cipresso

Copyright © 2015 Sofia Segkouli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessibility for older adults. The main aim of this study is to develop and test virtual user models (VUM) simulating mild cognitive impairment (MCI) through novel specific modules, embodied at cognitive models and defined by estimations of cognitive parameters. Well-established MCI detection tests assessed users’ cognition, elaborated their ability to perform multitasks, and monitored the performance of infotainment related tasks to provide more accurate simulation results on existing conceptual frameworks and enhanced predictive validity in interfaces’ design supported by increased tasks’ complexity to capture a more detailed profile of users’ capabilities and limitations. The final outcome is a more robust cognitive prediction model, accurately fitted to human data to be used for more reliable interfaces’ evaluation through simulation on the basis of virtual models of MCI users.