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Finding critical features for predicting quality of life in tablet-based serious games for dementia

  • Jeehoon Cha
  • Jan-Niklas Voigt-AntonsEmail author
  • Carola Trahms
  • Julie Lorraine O’Sullivan
  • Paul Gellert
  • Adelheid Kuhlmey
  • Sebastian Möller
  • Johanna Nordheim
Research Article
  • 26 Downloads

Abstract

While the number of dementia cases is steadily increasing, as of today no medication has been developed to cure its underlying causes. Instead, the focus in treatment has shifted to improve quality of life (QoL) for people with dementia (PwD). To this end, some non-pharmacological treatments such as exercising, socializing, and playing games have received increasing attention. PflegeTab is a tablet-based application developed for this purpose. It includes a number of services such as cognitive training games, everyday activity training games, emotional applications, and a biographical picture album. In the present paper, we explore the possibility of QoL prediction for PwD using data collected while nursing home residents played games in PflegeTab (\(N = 81\)). Using features generated from the data and applying linear discriminant analysis for classification, our approach obtained an average accuracy of 74.80% on predicting QoL ratings when measured by Monte Carlo cross-validation. Furthermore, this paper investigates which features were dominant for the classification (prominent features were e.g. time needed for task completion) and briefly discusses how the results might be utilized for managing general QoL of PwD.

Keywords

Dementia Touchscreen interaction Quality of life prediction Machine learning Feature selection 

Notes

Acknowledgements

This research was conducted as part of PflegeTab research project (https://pflegetab.qu.tu-berlin.de) funded by The National Association of Statutory Health Insurance Funds (GKV-Spitzenverband).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Authors and Affiliations

  1. 1.Quality and Usability LabTechnische Universität BerlinBerlinGermany
  2. 2.Institut für Medizinische Soziologie und RehabilitationswissenschaftCharité - Universitätsmedizin BerlinBerlinGermany
  3. 3.German Research Center for Artificial Intelligence (DFKI)BerlinGermany

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