School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
PLoS One. 2020 May 29;15(5):e0233545. doi: 10.1371/journal.pone.0233545. eCollection 2020.
Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in the context of human-robot interaction. They were utilised for auto-adaptation of the task difficulty, which resulted in a prolonged training interaction. The electromyogram data was collected from three gross-muscles of the upper limb in 30 healthy participants. The experiment followed a protocol for increasing the muscle strength by progressive strength training, that was an implementation of a known method in sports science for muscle training, in a new domain of robotic adaptation in muscle training. The study also compared how the participants in three experimental conditions perceived the change in task difficulty levels. One task benefitted from robotic adaptation (Intervention group) where the robot adjusted the task difficulty. The other two tasks were control groups 1 and 2. There was no difficulty adjustment at all in Control 1 group and the difficulty was adjusted manually in Control 2 group. The results indicated that the participants could perform a prolonged progressive strength training exercise with more repetitions with the help of a fatigue-based robotic adaptation, compared to the training interactions, which were based on manual/no adaptation. This study showed that it is possible to alter the level of the challenge using fatigue indicators, and thus, increase the interaction time. The results of the study are expected to be extended to stroke patients in the future by utilising the potential for adapting the training difficulty according to the patient's muscular state, and also to have a large number repetitions in a robot-assisted training environment.
关于提高上肢康复训练适应性的研究通常没有充分考虑肌肉疲劳的影响。在这项研究中，肌电图特征被用作人机交互背景下的疲劳指标。它们被用于自动适应任务难度，从而延长了训练交互。肌电图数据是从 30 名健康参与者的上肢三大肌肉中收集的。该实验遵循了一种通过渐进式力量训练来增强肌肉力量的方案，这是运动科学中一种用于肌肉训练的已知方法在机器人肌肉训练适应新领域的应用。该研究还比较了三组实验条件下的参与者如何感知任务难度水平的变化。一项任务受益于机器人适应（干预组），机器人调整了任务难度。其他两个任务是对照组 1 和 2。在对照组 1 中根本没有难度调整，而在对照组 2 中手动调整难度。结果表明，与基于手动/无适应的训练交互相比，参与者在基于疲劳的机器人适应的帮助下可以进行更长时间的渐进式力量训练，完成更多的重复次数。这项研究表明，使用疲劳指标改变挑战水平是可行的，从而增加了交互时间。未来，这项研究的结果有望通过利用根据患者肌肉状态调整训练难度的潜力，并在机器人辅助训练环境中实现大量重复训练，扩展到中风患者。
