Restoration of Reach and Grasp in Stroke Patients using Electrical Stimulation and Haptic Feedback

当你练习打网球时，你会打得更好，因为在你的大脑和脊髓中建立了新的神经连接。你不仅需要练习，你还需要你的表现反馈，这样你就可以纠正你的动作。在这项研究中，我们用这个想法来教中风患者如何学习新技能。当人们在中风后重新学习技能时，他们经历的过程与你学习打网球时相同。但是他们有一个问题。因为连接他们大脑和肌肉的一些神经已经受损，他们几乎不能移动。因此，他们不能练习，这意味着他们得不到反馈。通过电刺激通向肌肉的神经，可以使肌肉活动起来。电脉冲沿着神经传播的方式与大脑发出的电脉冲相似。如果仔细控制刺激，可以做出有用的动作。如果这个人自己尝试这个动作，效果会更好；因此，我们需要将一个人自己的努力与足够的额外电刺激结合起来来实现这个动作。在之前的一个研究项目中，我们设计并测试了控制算法（用于调节刺激的规则），以在简单的伸手任务中刺激一块肌肉。我们让病人用手追踪离开身体的光点。他们的前臂搁在桌子上滑动的水平支撑物上，他们的手弯曲着一根竖杆。当他们移动时，我们刺激伸展肘部肌肉的肌肉。在每次尝试后，我们使用“规则”来调整下一次刺激的水平和时间，以改善他们的跟踪。经过五次尝试，病人几乎可以完美地追踪到光点。然后他们继续练习；如果他们很好地跟踪了这个点，那么下一次他们从刺激中得到的帮助就会减少。这被称为迭代学习，它模拟了大脑学习新技能的方式。一项针对5名中风患者的研究表明，它帮助他们重新学习移动手臂，但他们在日常工作中并没有得到更好的表现。为了做到这一点，我们认为我们需要通过3D来让任务更加“真实”，包括张开手、抓握和伸手。这需要刺激肩膀、肘部、手腕和手指的肌肉。跟踪一个光点也很无聊，而且不会给你在现实生活中体验到的“触摸”反馈。因此，患者将玩一个虚拟现实电脑游戏，而不是追踪一个光点，当他们成功地抓住一个虚拟物体时，他们的指尖将受到感官刺激。为了使游戏更有趣，我们将调整难度水平，使其具有挑战性，并给人一种成功和进步的感觉。设计控制算法来调整对多个肌肉的刺激，使它们能够执行现实生活中的任务，提供适当和及时的感官反馈，并调整“游戏”是这个项目的目标。这些是为实现我们的目标所应采取的步骤。首先，为了设计规则，我们将创建一个手臂和手的数学模型来预测它将如何反应，考虑到痉挛（某些肌肉的不自主过度活动）和疲劳等因素。然后我们设计算法——这是项目中最具挑战性的部分。为了提供感官或“触觉”反馈，我们将调整一种市售手套（不需要重新发明），添加传感器，将手套上的信息与算法联系起来，使手套便于患者戴上和脱下。在整个项目中，我们将与患者、治疗师和其他人交谈，以确保我们创造的东西符合目的。每种成分都将在健康人群和中风患者中进行测试。实验工作将在为期8周的临床试验中达到高潮，其中包括5到8名中风患者。

When you practice playing tennis you become better at it, because new nerve connections have been made within your brain and spinal cord. Not only do you need to practice, but you also need feedback of your performance so that you can correct your movement. In this research we are using this idea to teach people who have had a stroke how to learn new skills. When people re-learn skills after a stroke they go through the same process as you do when you learn to play tennis. But they have a problem. Because some of the nerves connecting their brain and their muscles have been damaged they can hardly move at all. Consequently they cannot practice which means they don't get feedback. Muscles can be made to work by Electrical Stimulation of the nerves leading to them. Electrical impulses travel along nerves in a similar way to electrical impulses from your brain. If stimulation is carefully controlled, a useful movement can be made. This works better if the person is attempting the movement themselves; we therefore need to combine a person's own effort with just enough extra electrical stimulation to achieve the movement. In a previous research project we designed and tested control algorithms (rules used to regulate stimulation) to stimulate one muscle during a simple reaching task. We asked patients to track a spot of light with their hand as it moved away from their body. Their forearm rested on a horizontal support that glided over a table and their hand was curved around a vertical bar. As they moved we stimulated the muscle that extended their elbow muscle. After each attempt we used the 'rules' to adjust the level and timing of the stimulation on the next to improve their tracking. After five attempts patients could track the spot of light almost perfectly. They then continued to practice; if they tracked the spot well, then the next time they got less help from the stimulation. This is called Iterative Learning and it models the way the brain learns new skills.A study with 5 stroke patients showed that it helped them to relearn to move their arm, but they didn't get much better at performing everyday tasks. To do this we believe we need to make the tasks more 'real' by being 3D and include opening the hand and grasping as well as reaching. This requires stimulating the muscles of the shoulder, elbow, wrist and fingers. Tracking a spot of light is also boring and does not give the 'touch' feedback that you experience in real-life. So, rather than tracking a spot of light, patients will play a virtual reality computer game and when they successfully grasp a virtual object they will get a sensory stimulus to the finger tips. To the make the games more fun we will adjust the level of difficulty so make it challenging and give a feeling of success and progress. To design control algorithms to adjust stimulation to multiple muscles so that they can perform real-life tasks, provide appropriate and timely sensory feedback and adjust the 'game' is the goal of this project. These are the steps to be taken to achieve our goal. Firstly, to design the rules, we will create a mathematical model of the arm and hand to predict how it will respond, taking into account things like spasticity (involuntary over-activity in some muscles) and fatigue. We then design the algorithms - this is the most challenging part of the project. To provide the sensory or 'haptic' feedback we will adapt a commercially available glove (no need to re-invent this) adding sensors, linking the information from the glove to the algorithm and making the glove easy for patients to put on and take off. Throughout the project we will talk to patients, therapists and others to make sure what we create is fit-for-purpose. Each component will be tested with healthy people and those who have had a stroke. The experimental work will culminate in an 8-week clinical trial involving between five and eight stroke patients.