Personalised Serious Games and Gamification in Healthcare: Survey and Future Research Directions
Authors:
Stéphanie Carlier,
Femke De Backere,
Filip De Turck
Abstract:
Serious games and gamification (SGG) in eHealth have positive health impacts, but a personalized approach is needed due to diverse user contexts. This introduces challenges in achieving personalization in SGG. A literature search on Web of Science and PubMed identified 31 articles: 22 on serious games and 9 on gamification. These strategies are most applied in behavior change and rehabilitation, w…
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Serious games and gamification (SGG) in eHealth have positive health impacts, but a personalized approach is needed due to diverse user contexts. This introduces challenges in achieving personalization in SGG. A literature search on Web of Science and PubMed identified 31 articles: 22 on serious games and 9 on gamification. These strategies are most applied in behavior change and rehabilitation, with machine learning and AI showing promise for personalization. Reusability of personalisation algorithms and domain knowledge is underemphasized, reported in only 10 articles. Future research should standardize personalized SGG development, focusing on component reuse to streamline design and enhance evaluation.
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Submitted 27 November, 2024;
originally announced November 2024.
Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling
Authors:
Gilles Vandewiele,
Isabelle Dehaene,
György Kovács,
Lucas Sterckx,
Olivier Janssens,
Femke Ongenae,
Femke De Backere,
Filip De Turck,
Kristien Roelens,
Johan Decruyenaere,
Sofie Van Hoecke,
Thomas Demeester
Abstract:
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram datab…
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Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram database. However, we argue that these results are overly optimistic due to a methodological flaw being made. In this work, we focus on one specific type of methodological flaw: applying over-sampling before partitioning the data into mutually exclusive training and testing sets. We show how this causes the results to be biased using two artificial datasets and reproduce results of studies in which this flaw was identified. Moreover, we evaluate the actual impact of over-sampling on predictive performance, when applied prior to data partitioning, using the same methodologies of related studies, to provide a realistic view of these methodologies' generalization capabilities. We make our research reproducible by providing all the code under an open license.
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Submitted 28 November, 2020; v1 submitted 15 January, 2020;
originally announced January 2020.