Flow Optimizer Framework: Validation of a Dynamic Difficulty Adjustment System for Serious Games

Serious games are an important tool to overcome the low engagement and adherence to rehabilitation programs due to their repetitive nature and lack of positive reinforcement. Dynamic difficulty adjustment (DDA) systems can contribute by providing algorithms to adapt serious games, keeping players engaged and in a flow state. However, these systems are generally custom-made for specific purposes and goals, lacking the adaptability to be easily integrated into serious games. In response to this problem, we introduced the Flow Optimizer Framework (FOF), a game-agnostic DDA system developed for Unity. This framework facilitates the integration of DDA algorithms with serious games in Unity, enabling real-time monitoring and adaptation based on player state through data processing, rule-setting, and decision-making.

First, we conducted a technical validation of the framework, assessing its performance in handling real-time data streams and its responsiveness to different scenarios. Following this validation, we evaluated its effectiveness in enhancing the flow state by conducting a usability study. 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.