Aiming to perform an extraction of features which are strongly related to hemiparesis, this work describes a case study involving the efforts of patients in upper-limb rehabilitation, diagnosed with such pathology. Expressed as data (kinematic and dynamic measures), patients' performance were sensed and stored by a single InMotion Arm robotic device for further analysis. It was applied a Knowledge Discovery roadmap over collected data in order to preprocess, transform and perform data mining through machine learning methods. Our efforts culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having 8 features of rehabilitation performance feeding the input. Interpreting the obtained feature structure, it was observed that force-related attributes are more significant to the composition of the extracted pattern
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.
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.
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