| Id | 2460 | |
| Author | Carlier S.; Naessens V.; De Backere F.; De Turck F. | |
| Title | A Software Engineering Framework for Reusable Design of Personalized Serious Games for Health: Development Study | |
| Reference | Carlier S.; Naessens V.; De Backere F.; De Turck F. A Software Engineering Framework for Reusable Design of Personalized Serious Games for Health: Development Study,JMIR Serious Games 11 |
|
| Keywords | ||
| Link to article | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149872670&doi=10.2196%2f40054&partnerID=40&md5=1603c4e0c3e9b3ad450c63a4a6ba9576 |
|
| Abstract | Background: The use of serious games in health care is on the rise, as these games motivate treatment adherence, reduce treatment costs, and educate patients and families. However, current serious games fail to offer personalized interventions, ignoring the need to abandon the one-size-fits-all approach. Moreover, these games, with a primary objective other than pure entertainment, are costly and complex to develop and require the constant involvement of a multidisciplinary team. No standardized approach exists on how serious games can be personalized, as existing literature focuses on specific use cases and scenarios. The serious game development domain fails to consider any transfer of domain knowledge, which means this labor-intensive process must be repeated for each serious game. Objective: We proposed a software engineering framework that aims to streamline the multidisciplinary design process of personalized serious games in health care and facilitates the reuse of domain knowledge and personalization algorithms. By focusing on the transfer of knowledge to new serious games by reusing components and personalization algorithms, the comparison and evaluation of different personalization strategies can be simplified and expedited. In doing so, the first steps are taken in advancing the state of the art of knowledge regarding personalized serious games in health care. Methods: The proposed framework aimed to answer 3 questions that need to be asked when designing personalized serious games: Why is the game personalized? What parameters can be used for personalization? and How is the personalization achieved? The 3 involved stakeholders, namely, the domain expert, the (game) developer, and the software engineer, were each assigned a question and then assigned responsibilities regarding the design of the personalized serious game. The (game) developer was responsible for all the game-related components; the domain expert was in charge of the modeling of the domain knowledge using simple or complex concepts (eg, ontologies); and the software engineer managed the personalization algorithms or models integrated into the system. The framework acted as an intermediate step between game conceptualization and implementation; it was illustrated by developing and evaluating a proof of concept. Results: The proof of concept, a serious game for shoulder rehabilitation, was evaluated using simulations of heart rate and game scores to assess how personalization was achieved and whether the framework responded as expected. The simulations indicated the value of both real-time and offline personalization. The proof of concept illustrated how the interaction between different components worked and how the framework was used to simplify the design process. Conclusions: The proposed framework for personalized serious games in health care identifies the responsibilities of the involved stakeholders in the design process, using 3 key questions for personalization. The framework focuses on the transferability of knowledge and reusability of personalization algorithms to simplify the design process of personalized serious games. © Stéphanie Carlier, Vince Naessens, Femke De Backere, Filip De Turck. |
|
| Metodology | Technique |
Find research methods used
Show Candidate Transition Variables for article (AI method)
Find category for article (AI method)
Find social impact for article (AI method)
Show WordCloud from article (AI method)
Find semantically similar articles (Semantic search)
