A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions

One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based o...

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Bibliographic Details
Published in:Computers in biology and medicine 2024-10, Vol.181, p.109033, Article 109033
Main Authors: Martinez-Pascual, David, Catalán, José M., Lledó, Luis D., Blanco-Ivorra, Andrea, Vales, Yolanda, Garcia-Aracil, Nicolas
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Exergames
Galvanic skin response
Games
Genetic algorithms
Neurology
Parameters
Personalization
Physiology
Rehabilitation
Rehabilitation robots
Robotics
Self assessment
Self evaluation
Skin tests
Therapy
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Summary:One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user’s skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session. We propose a dynamic adaptation method that can be used during different rehabilitation sessions, without the need for recalibration between sessions. The adaptation architecture is based on a genetic algorithm that aims to maintain a certain score level and to motivate the user to move. The method has been evaluated with two serious games for five sessions using a rehabilitation robot. A common initial evaluation was made for all the users involved in the study, and the game parameters that best suited each user from the previous session were introduced as the starting point of the next session. In addition, the desired score rate was lowered between sessions to increase the difficulty level. The psychophysiological state of the users was measured based on the Self-Assessment Manikin test, as well as different cardiorespiratory and galvanic skin response signals were analyzed. The adaptation architecture proposed can find those game parameters that maximize the user movement for both games. In one of the games, the score rate set for each session is followed with high fidelity. The degree of personalization in the games increases between sessions as the dispersion of the game parameters grows. The Self-Assessment Manikin test and the physiological signals results would indicate that the psychophysiological state remains equal between sessions despite an increase in game difficulty. The genetic algorithm-based game adaptation has proven efficacy in maximizing the therapy performance through the sessions without needing recalibration. It also can be concluded that the design of the game influences the adaptation performance. Additionally, adaptive game design facilitated by our method does not significantly impact players’ emotional or physiological states. [Display omitted] •A genetic algorithm is proposed to personalize games for robot-aided rehabilitation•The method finds parameters encouraging wider movements while keeping success rates.•The proposed
ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2024.109033