Just Fabian, Ozen Ozhan, Tortora Stefano, Riener Robert, Rauter Georg
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:72-77. doi: 10.1109/ICORR.2017.8009224.
Highly impaired stroke patients at early stages of recovery are unable to generate enough muscle force to lift the weight of their own arm. Accordingly, task-related training is strongly limited or even impossible. However, as soon as partial or full arm weight support is provided, patients are enabled to perform arm rehabilitation training again throughout an increased workspace. In the literature, the current solutions for providing arm weight support are mostly mechanical. These systems have components that restrict the freedom of movement or entail additional disturbances. A scalable weight compensation for upper and lower arm that is online adjustable as well as generalizable to any robotic system is necessary. In this paper, a model-based feedforward weight compensation of upper and lower arm fulfilling these requirements is introduced. The proposed method is tested with the upper extremity rehabilitation robot ARMin V, but can be applied in any other actuated exoskeleton system. Experimental results were verified using EMG measurements. These results revealed that the proposed weight compensation reduces the effort of the subjects to 26% on average and more importantly throughout the entire workspace of the robot.
处于恢复早期的严重中风患者无法产生足够的肌肉力量来抬起自己手臂的重量。因此，与任务相关的训练受到很大限制甚至无法进行。然而，一旦提供部分或全部手臂重量支持，患者就能够在更大的工作空间内再次进行手臂康复训练。在文献中，目前提供手臂重量支持的解决方案大多是机械的。这些系统的部件会限制运动自由度或带来额外干扰。需要一种可扩展的上臂和下臂重量补偿方案，该方案可以在线调整并适用于任何机器人系统。本文介绍了一种基于模型的上臂和下臂前馈重量补偿方法，该方法满足上述要求。所提出的方法在上肢康复机器人ARMin V上进行了测试，但可应用于任何其他驱动外骨骼系统。实验结果通过肌电图测量得到验证。这些结果表明，所提出的重量补偿平均将受试者的用力减少到26%，更重要的是在机器人的整个工作空间内均是如此。
