Gamified In-Home Rehabilitation for Stroke Survivors: Analytical Review
Paul TAMAYO-SERRANO1, Samir GARBAYA2,
Hossein JAMSHIDI FARSANI1, Sema ALAçAM3,
Pierre BLAZEVIC1
1Laboratoire
END-ICAP, U1179 INSERM, University of Versailles Saint-Quentin-en-Yvelines,
Versailles, 78000 - France, {paul.tamayo-serrano, hossein.jamshidifarsani,
pierre.blazevic}@uvsq.fr
2Laboratoire
END-ICAP, U1179 INSERM, ENSAM - ParisTech, Paris, 75013 - France, samir.garbaya@ensam.eu
3Istanbul
Technical University, Turkey, alacams@itu.edu.tr
Abstract
A stroke is a
life-changing event that may end up as a disability, with repercussions on the
patient’s quality of life. Stroke rehabilitation therapies are helpful to regain some of the patient’s lost
functionality. However, in practice stroke
patients may suffer from a gradual loss of motivation. Gamified systems are
used to increase user motivation, hence, gamified elements have been
implemented into stroke rehabilitation therapies in order to improve patients’
engagement and adherence. This review work focuses on selecting and analyzing
developed and validated gamified stroke rehabilitation systems published
between 2009 and 2017 to identify the most important features of these systems.
After extensive research, 32 articles have met the selection criteria,
resulting in a total of 28 unique works. The works were analyzed and a total of
20 features were identified. The features are explained, making emphasis on the
works that implement them extensively. Finally, a classification of features
based on objectives is proposed, which was used to identify the relationships
between features and implementation gaps. It was found that there is a tendency
to develop low-cost solutions as in-home therapy systems; to include automated
features; provide a diversity of games
and use of simple interaction devices. This review allowed the definition of
the opportunities for future research direction such as systems addressing the
three rehabilitation areas; data analytics to make decisions; motivational content
identification based on automatic engagement detection and emotion recognition;
and alert systems for patient´s safety.
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