Flow Optimizer: A Dynamic Difficulty Adjustment Framework for Serious Games in Neurorehabilitation

Serious games are an important tool to overcome the low engagement and adherence to rehabilitation programs due to its repetitive nature and lack of positive reinforcement. Dynamic Difficulty Adjustment (DDA) systems can contribute as they provide algorithms to adapt serious games and keep the players engaged and in a flow state. However, DDA systems are generally custom-made for specific purposes and goals, and lack the adaptability to be easily integrated into serious games. In response to this problem, we introduce the Flow Optimizer Framework (FOF), a game-agnostic DDA system developed for Unity Engine. This framework facilitates the integration of DDA algorithms with serious games in Unity, the processing of data, rule-setting and decision-making, allowing real-time monitoring and adaptation of serious games and applications for health according to the player's state. Here we present its implementation and a study validating the FOF's effectiveness in enhancing the flow state. Participants were presented with three different types of DDA paradigms implemented in FOF (Implicit, Explicit and Subjective), each with different algorithms to adjust the game's difficulty. The results obtained showed that the implementation of a biofeedback paradigm using the player's heart rate was the one that increased game performance the most, and participants reported this condition as the most enjoyable and fitting to their skills. Overall, participants reported a high usability and a high presence experienced in the serious games implemented with FOF.