Design and Implementation of Adaptive Neuro-fuzzy Exergame Controller

Abstract

Introduction: Due to sedentary postures caused by video games, many health-related issues have occurred among players. One practical solution for dealing with the aforementioned problem is to come up with game controllers which promote physical exercises. In this study, an adaptive neuro-fuzzy exergame controller was introduced. Materials and Methods: During the training stage, the parameters of the fuzzy logic’s member functions were fine-tuned. By calculating a gradient vector and by applying backpropagation, the aforementioned parameters were updated using the measured error. The controller was made of four pads, each containing a resistor and a pushbutton, which were connected to a microcontroller. In order to improve the user experience, an adaptive neuro-fuzzy logic system was used to analyze the gathered data from the controller. Results: A pure fuzzy logic system (FLS) cannot provide an acceptable playing experience for players of different ages and physical characteristics. The received signal from the controller was sent to a fine-tuned FLS. The calculated output of the previously trained FLS was one of the defined classes of “ignore”, “press”, and “hold”, which was sent as a command to the main computer. Conclusion: In the proposed method, the FLS was fine-tuned by gathering data from the user, which improved the performance of the controller due to the fact that the controller was trained to best suit the needs of the user. The gathered data was then used to change the parameters of the FLS to provide an acceptable playing experience for the user.