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Abstract-This article proposes a reinforcement learning approach to dynamically model the player skills in applications that integrate games and rehabilitation robotic. The approach aims to match the game difficulty to the player skills, keeping proper motivation (flow) during a rehabilitation process. The traditional rehabilitation process involves repetitive exercises. Robots and serious games provide new means to improve user motivation and commitment during treatment. Each person shows different skills when facing the challenges posed by computer games. Thus, the game difficulty level should be adjusted to each player skill level. The Q-Learning algorithm was adapted in this context to modify game parameters and to assess user skills based on a performance function. This function provides a path to an individual difficulty adjustment and consequently a tool to keep the user exercising. Experiments with thirty minutes duration are presented, involving four players, and the results obtained indicate the proposed approach is feasible for modeling the user behaviour getting to capture the adaptations and trends for each player according to the game difficulties.

In this paper, we discuss a strategy for the adaptation of the "difficulty level" in games intended to include motor planning during robotic rehabilitation. We consider concurrently the motivation of the user and his/her performance in a Pong game. User motivation is classified in three levels (not motivated, well motivated and overloaded). User performance is measured as a combination of knowledge of results--achieved goals and score points in the game--and knowledge of performance--joint displacement, speed, aiming, user work, etc. Initial results of a pilot test with unimpaired healthy young volunteers are also presented showing a tendency for individualization of the parameter values.

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This article proposes the use of two evolutionary algorithms (EAs) to the dynamic difficulty adjustment (DDA) of a serious game in the rehabilitation robotics application. DDA occurs in runtime for a better user experience with a game. This approach is used to improve the quality of the game experience and to avoid boredom or frustration for players with severe limitations imposed by pathologies such as stroke, cerebral palsy, and spinal cord injuries. The first EA solves the game adjustment problem, changing the game difficulty according to the player’s skill, and the purpose of the second EA is to adjust the coefficients of the first EA’s objective function so that it can work in a more effective way. To do so, the second EA uses results of game matches against simulated player profiles. The results shows that the presented method was able to identify a set of coefficients that allows the first EA to correctly adjust the difficulty level for all six tested player profiles.