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Dynamic Difficulty Adaptation in Serious Games for Motor Rehabilitation

Authors: Hocine, Nadia; Gouaich, Abdelkader; Cerri, Stefano A.;

Dynamic Difficulty Adaptation in Serious Games for Motor Rehabilitation

Abstract

In the last few years, a growing interest has been devoted to improve rehabilitation strategies by including serious games in the therapy process. Adaptive serious games seek to provide the patients with an individualized rehabilitation environment that meets their training needs. In this paper, a dynamic difficulty adaptation (DDA) technique is suggested. This technique focuses on the online adaptation of the game difficulty by taking into account patients' abilities and motivation. The results of the experiment show that the adaptation technique increases the number of tasks, number of successful tasks as well as the movement amplitude during a game session. The technique positively effects the training outcomes of stroke patients, which can help them to recover their functions.

Keywords

Stroke, Serious games, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, Adaptation, [INFO] Computer Science [cs]

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
Green