Designed Intelligence: Data for Serious Gaming (DI-D4SG)
Main research interest (DSC/e related)
Interactions with systems, products and services yield enormous amounts of data that can improve the design and help create better products and services in the future. Our focus is on highly interactive systems, engaging people through games and (complex) social interactions to express themselves and enact positive change. Through thorough data collection, analysis and modeling, the impact of real-world interventions can be quantified and made relevant for researchers and design stakeholders.
We explore novel interaction concepts targeting the subconscious, learning and education, serious games and well-being – and use data to apply machine learning in the design of interactive systems. Our vision is to generate new insights, often as design knowledge, that help scale contextual, situated and personalized modes of interaction.
TIMO was designed as a computer game specifically for diagnostic purposes with immersive environments, and multimodal interaction being used as part of the psychological assessment in ADHD diagnosis. This project was a collaboration between University of Genoa and TU/e under Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environment (EMJD-ICE) Program, and Kempenhaeghe, Center for Neurological Learning Disabilities.
Automatic Mental Health Assistant project was part of the MARS-500 experiment carried out at the Institute for Biomedical Problems in Moscow. The European Space Agency and the Russian Academy of Sciences jointly conducted this large scale experiment in order to simulate a manned mission to Mars. We contributed with a diagnostic game based decision tool to measure crew coherence in subconscious states.
- Smart Technologies for Stress Free Air Travel, sponsored by EC-’Aeronautics and Space’. Development of a new service for the Inflight-Entertainment-System based on bio-signals (e.g., heart rate) to reduce passengers’ stress on long haul flights.
- With Learning Analytics in the context of puzzle games we empirically showed how different players converge towards different winning or losing strategies. This analysis was supported by fully deterministic instrumentation of the game play and the combined application of clustering analysis and process mining.