Performance-based selective training for robot-mediated upper limb motor learning and stroke rehabilitation

中风是成年人残疾的主要原因，每年给欧洲经济造成超过300亿欧元的损失。大多数中风幸存者患有感觉和运动缺陷，导致长期的，有时是严重的运动障碍。上肢的部分瘫痪或无力伴运动执行受损是常见的，这使得许多中风患者无法轻松地进行日常活动，例如伸手和抓握物体，从而限制了他们的生活质量。最近一项汇总了几项独立研究的荟萃分析得出结论，考虑到收益和风险的平衡，应向美国门诊患者和慢性中风幸存者提供机器人辅助康复，由“机器人”指导他们的运动，并提供规范的实践制度。这些机器人设备是机动的、关节式的、杠杆式的，其手柄由患者握持或附接到手臂，并且马达提供引导所需运动的温和力。虽然有益，但这些研究中使用的实践时间表通常非常简单，并且很少考虑患者的个人能力，这与标准的、由康复者引导的康复不同。相比之下，美国最近的一项研究表明，在机器人辅助的制度内，单独调整的基于性能的实践可能是有益的，促进和保持受影响手臂运动的恢复。这种实践制度，其中手臂运动最初由机器人引导，并且随着时间的推移辅助量减少，与其他机器人疗法相比，在给定的康复期内导致了更大的运动改善。但是，所训练的动作的选择仍然是受管制的，并不能反映病人的能力。已经知道，对于训练而言，练习变化的任务比练习一致的任务更有效。此外，如果在选择练习运动时不考虑个人损伤/表现，则存在练习可能导致运动功能恶化的风险因此，我们的目标是测试组合单独定制的练习运动选择的优点，以考虑患者独特的和变化的表现水平，最先进的机器人辅助治疗为此，我们将首先扩展现有的试点数据，显示基于个人表现“地图”选择运动练习条件的算法可以产生最多的学习。这将首先使用未受损的健康参与者在许多不同的练习方案中进行测试，以确认最有利的训练。我们还将测试这种学习是否适用于未练习的动作，并在练习后至少保留一周。接下来，我们将研究这些2D运动“地图”与潜在的上臂关节运动和主要肌肉活动之间的关系，以更好地了解肢体生物力学，肌肉神经控制和性能地图之间的关系。我们将在健康参与者和中风幸存者中研究这些关系，以建立正常表现的模板。最后，我们将测试我们的适应性，个性化和基于表现的训练对中风幸存者的益处，测试16周训练的持续改善，测试改善的表现对未练习运动的概括，以及使用已建立的临床评分方案来量化改善的运动及其从实验室到日常动作的转移。我们项目的结果将为使用更大的患者群体进行完整的临床试验提供基础，作为后续研究。

Stroke is a major cause of disability in the adult population, costing the European economy over Euro30 billion per year. The majority of stroke survivors suffer from sensory and movement deficits that result in long-term, and sometimes severe, movement disorders. Partial paralysis or weakness of the upper limb with impaired execution of movements is common, leaving many stroke patients unable to easily perform everyday actions such as reaching and grasping for objects, and so limiting their quality of life.A recent meta-analysis summarising several independent studies has concluded that given the balance of benefits and risks, robot-assisted rehabilitation should be provided to US outpatients and chronic stroke survivors, with a "robot" guiding their movements and providing a regulated practice regime. These robotic devices are motorized, jointed, levers, the handle of which is held by the patient or attached to the arm, and the motors provide gentle forces that guide the desired movement, Although beneficial, the practice schedules used in these studies are typically quite simple and rarely take account of the patient's individual abilities, unlike standard, practitioner-led rehabilitation. In contrast, a recent USA study has shown that individually adjusted performance-based practice within a robot-assisted regime can be beneficial, facilitating and retaining recovery of the affected arm's movement. This practice regime, where the arm movement was initially guided by the robot, and the amount of assistance was reduced over time, led to substantially greater motor improvement for a given period of rehabilitation compared to other robotic therapies. But the SELECTION of movements that were trained was still regulated, and did not reflect the patient's abilities. It is already known that practice of a task that is varied is more effective for training than practice of a consistent task. Moreover, if individual impairment/performance is not taken into account in the selection of practiced movement, there is a risk that practice might lead to worsened motor function (e.g. by encouraging pathologic movement patterns).We therefore aim to test the advantages of combining individually tailored selection of practice movements, to take account of the patients' unique and changing levels of performance, with state-of-art robot-assisted therapy. To do so, we will first expand on our existing pilot data showing that an algorithm that selects movement practice conditions based on individual performance "maps" can generate the most learning. This will first be tested using unimpaired, healthy participants across a number of different practice regimes to confirm the most advantageous training. We will also test that this learning generalises to unpracticed movements, and is retained for at least a week after practice. We will next study the relationship between these 2D movement "maps" and the underlying upper arm joint movements and major muscle activity, to better understand the relationship between the limb's biomechanics, the neural control of muscles, and the performance maps. We will study these relationships in both healthy participants, to build up a template of normal performance, and in stroke survivors.Finally we will test the benefit of our adaptive, individuated, and performance-based training on stroke survivors, testing for sustained improvement over 16 weeks of training, testing for generalisation of the improved performance to unpracticed movements, and quantifying the improved movement and its transfer from the lab into daily actions using established clinical scoring schemes. The results of our project would then provide the basis for a full clinical trial, using larger groups of patients, as a follow up study.