Usability Evaluation of an Adaptive Serious Game Prototype Based on Affective Feedback

Abstract

Difficulty in video games is an essential factor for a game to be considered engaging and is directly linked to losing in a game. However, for the user to not feel bored or frustrated, it is necessary for the difficulty of the game to be balanced and ideally tailored to the user. This paper presents the design and development of a serious game that adjusts its difficulty based on the user’s bio signals, so that it is demanding enough to match his/her skills, in order to enter the flow state. The serious game is accompanied by a server that uses machine learning algorithms to analyze the user’s bio signals and classify them into different affective states. These states are later used to adjust the difficulty of the serious game in real-time, without interfering with the user’s game experience. Finally, a heuristic evaluation was conducted in order to measure its usability and highlight the good practices and to draw attention to some elements of the game that should be changed in a future version.