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首页> 外文期刊>Journal of NeuroEngineering Rehabilitation >Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton
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Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton

机译:机器人步态训练中步态子任务的选择性控制:具有动力外骨骼的中风幸存者的足部间隙支持

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Background Robot-aided gait training is an emerging clinical tool for gait rehabilitation of neurological patients. This paper deals with a novel method of offering gait assistance, using an impedance controlled exoskeleton (LOPES). The provided assistance is based on a recent finding that, in the control of walking, different modules can be discerned that are associated with different subtasks. In this study, a Virtual Model Controller (VMC) for supporting one of these subtasks, namely the foot clearance, is presented and evaluated. Methods The developed VMC provides virtual support at the ankle, to increase foot clearance. Therefore, we first developed a new method to derive reference trajectories of the ankle position. These trajectories consist of splines between key events, which are dependent on walking speed and body height. Subsequently, the VMC was evaluated in twelve healthy subjects and six chronic stroke survivors. The impedance levels, of the support, were altered between trials to investigate whether the controller allowed gradual and selective support. Additionally, an adaptive algorithm was tested, that automatically shaped the amount of support to the subjects’ needs. Catch trials were introduced to determine whether the subjects tended to rely on the support. We also assessed the additional value of providing visual feedback. Results With the VMC, the step height could be selectively and gradually influenced. The adaptive algorithm clearly shaped the support level to the specific needs of every stroke survivor. The provided support did not result in reliance on the support for both groups. All healthy subjects and most patients were able to utilize the visual feedback to increase their active participation. Conclusion The presented approach can provide selective control on one of the essential subtasks of walking. This module is the first in a set of modules to control all subtasks. This enables the therapist to focus the support on the subtasks that are impaired, and leave the other subtasks up to the patient, encouraging him to participate more actively in the training. Additionally, the speed-dependent reference patterns provide the therapist with the tools to easily adapt the treadmill speed to the capabilities and progress of the patient.
机译:背景技术机器人辅助步态训练是神经病患者步态康复的新兴临床工具。本文讨论了一种使用阻抗控制外骨骼(LOPES)提供步态辅助的新颖方法。提供的帮助基于最近的发现,即在行走控制中,可以识别出与不同子任务关联的不同模块。在这项研究中,提出并评估了用于支持这些子任务之一(即脚部间隙)的虚拟模型控制器(VMC)。方法开发的VMC在脚踝处提供虚拟支撑,以增加脚部间隙。因此,我们首先开发了一种新方法来导出脚踝位置的参考轨迹。这些轨迹由关键事件之间的样条曲线组成,这些样条曲线取决于步行速度和身高。随后,在12名健康受试者和6名慢性中风幸存者中对VMC进行了评估。在两次试验之间更改了支撑物的阻抗水平,以调查控制者是否允许逐步和选择性支撑物。此外,还测试了一种自适应算法,该算法可以自动调整对象的需求量。引入了捕捉试验,以确定受试者是否倾向于依靠支持。我们还评估了提供视觉反馈的附加价值。结果使用VMC,可以有选择地逐渐影响台阶高度。自适应算法明显地将支持水平调整为每个卒中幸存者的特定需求。提供的支持并未导致对这两个群体的支持。所有健康受试者和大多数患者都能够利用视觉反馈来增加他们的积极参与。结论提出的方法可以提供对步行的基本子任务之一的选择性控制。此模块是控制所有子任务的模块集中的第一个模块。这使治疗师能够将支持集中在受损的子任务上,而将其他子任务留给患者,从而鼓励他更积极地参加培训。另外,与速度有关的参考模式为治疗师提供了可轻松使跑步机速度适应患者能力和病情的工具。

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