Interpreting Data of Serious Games for Health using Decision Support Systems

• Main Authors: Konrad Peters (Author), Fares Kayali (Author), Marisa Silbernagl (Author), Anita Lawitschka (Author), Helmut Hlavacs (Author)
• Format: Book
• Published: Serious Games Society, 2017-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.17083/ijsg.v4i2.162; 2384-8766
• Keywords: Serious Games for Health; Standardization; Data Modeling; Decision Support Systems; Education; L; Electronic computers. Computer science; QA75.5-76.95; Computer software; QA76.75-76.765
• Source: International Journal of Serious Games, Vol 4, Iss 2 (2017)

In order to empower the influence and usage of Serious Games for Health (SGFH) in science as well as application, a decision-support-system (DSS) based approach for interpreting game data should be developed. This DSS would allow users (patients, physicians) to interpret medical data, gathered by various serious games, as well as the game-scores of these games. The usage of DSSs implies the requirement for a standardized data model, for both medical data as well as game-proprietary data, such as meta-data or game-scores. This publication presents a framework proposal, which covers the requirements to interpret data of various health- and game-sources and create recommendations to users. Authors identified challenges and experiments to be done: to provide a ubiquitous data model for SGFH, a set of existing games will be analysed and evaluated. Further, a SDK for game developers will be created. This would enable the developers to gain access to the DSS based approach with reasonable effort. Finally, a DSS, consisting of two sub-DSS, should be implemented on top of the previous results.