Working memory is an important function for human cognition since several day-to-day\nactivities are related to it, such as remembering a direction or developing a mental calculation.\nUnfortunately, working memory deficiencies affect performance in work or education related\nactivities, mainly due to lack of concentration, and, with the goal to improve this, many software\napplications have been developed. However, sometimes the user ends up bored with these games\nand drops out easily. To cope with this, our work explores the use of intelligent robotics and dynamic\ndifficulty adjustment mechanisms to develop a novel working memory training system. The proposed\nsystem, based on the Nao robotic platform, is composed of three main components: First, the N-back\ntask allows stimulating the working memory by remembering visual sequences. Second, a BDI model\nimplements an intelligent agent for decision-making during the progress of the game. Third, a fuzzy\ncontroller, as a dynamic difficulty adjustment system, generates customized levels according to the\nuser. The experimental results of our system, when compared to a computer-based implementation of\nthe N-back game, show a significant improvement on the performance of the user in the game, which\nmight relate to an improvement in their working memory. Additionally, by providing a friendly and\ninteractive interface, the participants have reported a more immersive and better game experience\nwhen using the robotic-based system.
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