Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Switzerland and Reharobotics Group, Spinal Cord Injury Center, Balgrist University Hospital, Medical Faculty, University of Zurich, Switzerland, Lengghalde 5, Zurich, 8092, Switzerland.
Motor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, Bern, 3010, Switzerland.
J Neuroeng Rehabil. 2020 Feb 5;17(1):13. doi: 10.1186/s12984-020-0644-3.
Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, performance, and control algorithms among the existing robotic platforms make it hard to effectively assess and compare human arm weight relief. In this paper, we introduce criteria for ideal arm weight compensation, and furthermore, we propose and analyze three distinct arm weight compensation methods (Average, Full, Equilibrium) in the arm rehabilitation exoskeleton 'ARMin'. The effect of the best performing method was validated in chronic stroke subjects to increase the active range of motion in three dimensional space.
All three methods are based on arm models that are generalizable for use in different robotic devices and allow individualized adaptation to the subject by model parameters. The first method Average uses anthropometric tables to determine subject-specific parameters. The parameters for the second method Full are estimated based on force sensor data in predefined resting poses. The third method Equilibrium estimates parameters by optimizing an equilibrium of force/torque equations in a predefined resting pose. The parameters for all three methods were first determined and optimized for temporal and spatial estimation sensitivity. Then, the three methods were compared in a randomized single-center study with respect to the remaining electromyography (EMG) activity of 31 healthy participants who performed five arm poses covering the full range of motion with the exoskeleton robot. The best method was chosen for feasibility tests with three stroke patients. In detail, the influence of arm weight compensation on the three dimensional workspace was assessed by measuring of the horizontal workspace at three different height levels in stroke patients.
All three arm weight compensation methods reduced the mean EMG activity of healthy subjects to at least 49% compared with the no compensation reference. The Equilibrium method outperformed the Average and the Full methods with a highly significant reduction in mean EMG activity by 19% and 28% respectively. However, upon direct comparison, each method has its own individual advantages such as in set-up time, cost, or required technology. The horizontal workspace assessment in poststroke patients with the Equilibrium method revealed potential workspace size-dependence of arm height, while weight compensation helped maximize the workspace as much as possible.
Different arm weight compensation methods were developed according to initially defined criteria. The methods were then analyzed with respect to their sensitivity and required technology. In general, weight compensation performance improved with the level of technology, but increased cost and calibration efforts. This study reports a systematic way to analyze the efficacy of different weight compensation methods using EMG. Additionally, the feasibility of the best method, Equilibrium, was shown by testing with three stroke patients. In this test, a height dependence of the workspace size also seemed to be present, which further highlights the importance of patient-specific weight compensation, particularly for training at different arm heights.
ClinicalTrials.gov,NCT02720341. Registered 25 March 2016.
利用康复机器人为中风患者进行手臂重量补偿，已成功用于增加主动运动范围并减少病理性肌肉协同作用的影响。然而，现有的机器人平台在结构、性能和控制算法方面的差异使得难以有效地评估和比较人体手臂重量减轻的效果。在本文中，我们介绍了理想的手臂重量补偿标准，并进一步提出并分析了手臂康复外骨骼“ARMin”中的三种不同的手臂重量补偿方法（平均、全、平衡）。通过在慢性中风患者中进行验证，该最佳方法可增加三维空间中的主动运动范围。
所有三种方法均基于手臂模型，这些模型可推广用于不同的机器人设备，并允许通过模型参数进行针对受试者的个性化适配。第一种方法平均使用人体测量表来确定特定于受试者的参数。第二种方法全的参数是根据预定义静止姿势中的力传感器数据来估计的。第三种方法平衡通过优化预定义静止姿势中的力/扭矩方程的平衡来估计参数。首先确定了所有三种方法的参数，并针对时间和空间估计灵敏度进行了优化。然后，在一项涉及 31 名健康参与者的随机单中心研究中，将三种方法在使用外骨骼机器人进行五个手臂姿势时的其余肌电图（EMG）活动方面进行了比较，这些姿势涵盖了运动范围的全部范围。选择了最佳方法进行三项中风患者的可行性测试。具体来说，通过在三名中风患者中测量水平工作空间，评估了手臂重量补偿对三维工作空间的影响，在三个不同的高度水平进行。
与无补偿参考相比，所有三种手臂重量补偿方法均将健康受试者的平均 EMG 活动降低至至少 49%。与平均和全方法相比，平衡方法的平均 EMG 活性降低了 19%和 28%，效果显著。但是，在直接比较时，每种方法都有其自身的优点，例如设置时间、成本或所需技术。对中风后患者使用平衡方法进行的水平工作空间评估显示，手臂高度可能存在工作空间尺寸依赖性，而重量补偿有助于尽可能最大化工作空间。
根据最初定义的标准开发了不同的手臂重量补偿方法。然后根据其灵敏度和所需技术对方法进行了分析。一般来说，随着技术水平的提高，重量补偿性能会提高，但是成本和校准工作也会增加。这项研究报告了一种使用肌电图分析不同重量补偿方法效果的系统方法。此外，通过对三名中风患者的测试，还证明了最佳方法平衡的可行性。在该测试中，工作空间的大小似乎也存在高度依赖性，这进一步强调了针对特定患者的重量补偿的重要性，特别是在不同手臂高度进行训练时。
ClinicalTrials.gov，NCT02720341。2016 年 3 月 25 日注册。
