A Novel Approach to Working Memory Training Based on Robotics and AI

Abstract

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