A self-adaptive serious game for eye-hand coordination training

Abstract

In this study, we propose a self-adaptive game aiming auxiliary in eye-hand coordination and short-term training. The game requires a player to pop specific balloons appearing in a screen aiming to prevent that they fly. The game uses the Dynamic Difficulty Adjustment (DDA) technique for verifying reaction time, pragmatic six-step plan for implementing adaptive difficulty proposed, and a Leap Motion controller as an input device. The adaptive difficulty of the game is implemented through Q-Learning, a Reinforcement Learning algorithm. We conducted experimental tests with ten participants (ages between 16 and 23 years). We evaluated the game’s learning effect by comparing velocity by episodes and accumulated reward by episodes for each player. We also evaluated the user experience using the System Usability Scale (SUS). Experimental results suggest that players accumulated positive rewards according to the velocity of the game adjusted to the profile of the user, taking it to a level of adequate and challenging difficulty at the same time. The SUS score per player shows that the game adapted dynamically difficulty being satisfactory. The study shows that the game implemented can contribute to eye-hand coordination training positively.