School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
J Neuroeng Rehabil. 2012 Jun 7;9:32. doi: 10.1186/1743-0003-9-32.
Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients' voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort.
Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants' arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression.
From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced.
The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.
利用电刺激（ES）和机器人技术的新型中风康复技术在减少上肢损伤方面非常有效。当 ES 应用于支持患者的自主努力时，效果最佳；然而，当前的系统未能充分利用这种联系。本研究基于先前使用先进的 ES 控制器的工作，旨在研究通过迭代学习进行刺激辅助（SAIL）的可行性，这是一种新型的上肢中风康复系统，利用机器人支持、ES 和自主努力。
五名上肢功能受损的偏瘫慢性中风参与者参加了 18 次、每次 1 小时的干预课程。参与者完成虚拟现实跟踪任务，即通过他们的受损手臂跟随一个缓慢移动的球体沿着指定的轨迹移动。为此，参与者的手臂由机器人支撑。ES 通过先进的迭代学习控制（ILC）算法进行介导，应用于三头肌和前三角肌。每次运动重复 6 次，ILC 会调整每次试验中应用的刺激量，以提高准确性并最大限度地发挥自主努力。参与者在基线和干预后进行临床评估（Fugl-Meyer，行动研究手臂测试），以及在每个干预课程开始和结束时进行无辅助跟踪任务。使用 t 检验和线性回归进行数据分析。
从基线到干预后，Fugl-Meyer 评分提高，辅助和无辅助跟踪性能提高，辅助跟踪所需的 ES 量减少。
使用 ILC 算法最小化 ES 支持的概念得到了证明。鉴于减少中风后的上肢损伤，积极的结果很有希望，但需要更大的研究来证实这一点。
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