PhD thesis defense to be held on February 13, 2023, at 12:00 (Multimedia/Tele-teaching room, NTUA Central Library Building)
Picture Credit: Konstantinos Mitsis
Thesis title: Leveraging sensor data for real time recognition of engagement in adaptive serious games for health.
Abstract: The aim of the present Doctoral Thesis is the development of a novel conceptual framework for personalization in serious games for health (SGH). The proposed framework leverages sensor data for the recognition of player engagement during interaction with SGH in real time. This approach aims to automatically generate game content based on player engagement, in-game performance, and health-related needs. In the present thesis, two novel SGH are designed and developed, aiming to promote self-health management in chronic health conditions and incorporating mechanics to facilitate procedural generation of content. A novel technique, based on a genetic algorithm, that employs heterogeneous data for procedural content generation in SGH is presented. Two carefully designed experimental processes are implemented to investigate the feasibility of the proposed framework. The proposed genetic algorithm technique is also incorporated in an intervention platform that includes a SGH and evaluated based on data from sensors that monitor disease self-management and physical activity. Finally, deep reinforcement learning agents are employed to automatically evaluate procedurally generated content in SGH.
Supervisor: Professor Konstantina Nikita
PhD Student: Konstantinos Mitsis