Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernández University, Avda. de la Universidad, Elche, 03202, Alicante, Spain.
Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernández University, Avda. de la Universidad, Elche, 03202, Alicante, Spain.
Comput Biol Med. 2024 Oct;181:109033. doi: 10.1016/j.compbiomed.2024.109033. Epub 2024 Aug 27.
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.
神经康复治疗中最大的挑战之一是为每位患者找到适当的治疗强度水平，以确保受影响肢体的运动恢复，同时保持其动机。不同的研究提出了根据心理生理状态、成功率或对用户技能建模来调整运动难度的方法。然而，所有研究都提出了针对单一治疗方案的解决方案，需要在每次治疗方案中使用前进行校准。我们提出了一种动态自适应方法，可以在不同的康复治疗方案中使用，无需在治疗方案之间重新校准。
自适应架构基于遗传算法，旨在保持一定的分数水平，并激励用户运动。该方法已使用康复机器人的两款严肃游戏进行了 5 次治疗方案的评估。所有参与研究的用户都进行了一次共同的初始评估，然后将前一次治疗方案中最适合每个用户的游戏参数作为下一次治疗方案的起点。此外，在治疗方案之间降低了目标分数率，以增加难度级别。用户的心理生理状态是基于自我评估量表进行测量的，同时还分析了不同的心肺和皮肤电反应信号。
所提出的自适应架构可以找到最大化用户运动的游戏参数，这对两款游戏都有效。在其中一款游戏中，每个治疗方案设置的分数率都得到了高度的保真度。随着游戏参数的分散程度增加，游戏的个性化程度在治疗方案之间增加。自我评估量表测试和生理信号结果表明，尽管游戏难度增加，但心理生理状态在治疗方案之间保持不变。
基于遗传算法的游戏自适应在无需重新校准的情况下，通过治疗方案证明了其在最大化治疗效果方面的有效性。还可以得出结论，游戏设计会影响自适应性能。此外，我们的方法促进的自适应游戏设计不会对玩家的情绪或生理状态产生显著影响。
