Impact of biofeedback and task-specific training with a robotic hand orthosis on voluntary muscle modulation for rehabilitation post-stroke

项目总结 用于手康复的机器人设备在改善获得运动训练的机会和鼓励 受损肢体的功能使用。这些设备可以为日常活动和增强提供帮助 传统的康复方法。可穿戴外骨骼是一个特别令人兴奋的研究领域，因为 他们可以提供超越诊所或实验室限制的治疗。我们使用的表面肌电图仪 (EMG)传感器和意图检测算法使中风后偏瘫患者能够直观地 控制可穿戴的机械手矫形器。然而，采用这种或类似设备的一个主要障碍是 过度痉挛，通过用户在努力尝试时招募所有可用肌肉而放大 控制机器人。当尝试运动模式时，肌肉过度协同激活是一种常见的 中风的并发症。我们的工作旨在通过使用肌电生物反馈来解决这个问题，显示真实的 将有关肌肉激活的时间信息发送给用户，以共同训练人类-机器人系统以生成运动模式 当把握到这一点时，将过度的协同激活降至最低。受肌肉活动视觉生物反馈研究的启发， 这揭示了康复训练的有希望的结果，我们对慢性中风患者的初步工作 已经表明，一些个体保留了一些改变肌肉激活模式的能力 肌电生物反馈。这项研究的目标是确定是否可以利用肌电生物反馈来 帮助训练中风幸存者调节肌肉激活，并使用机器人产生所需的运动模式 帮助，同时最大限度地减少不必要的协同激活和痉挛。这一目标将通过追求实现 两个目标。Aim 1对生物反馈和机器人训练采取了一种辅助方法。我们将确定 当中风幸存者使用机器人的肌电生物反馈时，可以实现屈肌/伸肌脱钩 援助。我们希望肌电生物反馈能帮助辨别和产生运动模式，从而导致 共激活异常最少。在目标1中，受试者将参加一个单节实验，该实验强调 机器人辅助的手部运动与协调的屈肌/伸肌激活对齐。目标2采取了一项 康复方法，并将调查多节训练是否使用肌电生物反馈和机器人 训练产生的康复效果和功能结果在矫形器移除后仍然存在。至 为了实现这一目标，我们将进行多节训练方案，在该方案中，矫形器需要逐步地 高保真的激活信号，以帮助完成运动。拟议的项目将提供洞察力 在训练期间人类-机器人流畅性的进步以及在训练后对运动学习的更好理解 卒中。此培训为我的发展计划提供了一个与 跨学科指导和协作团队在机器人工程的交叉点开展项目 和中风康复，最终将使我做好准备领导一个翻译研究实验室开发 以患者为中心的方法，建立设备，以改善神经障碍患者的运动控制。

PROJECT SUMMARY Robotic devices for hand rehabilitation show promise in improving access to motor training and encouraging functional use of the impaired limb. These devices can provide assistance for daily activities and augment traditional rehabilitation methods. Wearable exoskeletons are a particularly exciting area of research because they could provide therapy beyond the confines of a clinic or laboratory. Our use of surface electromyography (EMG) sensors and intent detection algorithms has enabled individuals with post-stroke hemiparesis to intuitively control a wearable robotic hand orthosis. However, a major barrier for adoption of this or similar devices is excessive spasticity, which is amplified by users’ recruitment of all available muscles when exerting effort to control the robot. This excessive coactivation of muscles when attempting movement patterns is a common complication for stroke. Our work aims to address this problem by using EMG biofeedback, the display of real- time information about muscle activation to the user, to co-train human-robot systems to generate motor patterns when grasping that minimize excessive coactivation. Inspired by studies on visual biofeedback of muscle activity, which have revealed promising results in rehabilitative training, our preliminary work with chronic stroke subjects has indicated that some individuals retain some capacity to change muscle activation patterns in response to EMG biofeedback. The goal of this research is to determine whether EMG biofeedback can be harnessed to help train stroke survivors to modulate muscle activation and generate desired movement patterns with robot assistance while minimizing unwanted coactivation and spasticity. This goal will be accomplished by pursuing two aims. Aim 1 takes an assistive approach to biofeedback and robotic training. We will determine the extent of flexor/extensor decoupling that is achievable when stroke survivors use EMG biofeedback with robotic assistance. We expect EMG biofeedback to aid discrimination and generation of motor patterns that result in the least abnormal coactivation. In Aim 1, subjects will participate in a single-session experiments that reinforce robot-assisted hand movements in alignment with coordinated flexor/extensor activation. Aim 2 takes a rehabilitative approach, and will investigate whether multi-session practice with EMG biofeedback and robotic training produces rehabilitative effects and functional outcomes that persist after the orthosis is removed. To achieve this, we will conduct a multi-session training regimen in which the orthosis requires a progressively higher-fidelity activation signal in order to assist movement completion. The proposed project will provide insights into the progression of human-robot fluency during training and greater understanding of motor learning after stroke. This training complements my development plan by providing an opportunity to work with an interdisciplinary mentoring and collaboration team to pursue a project at the intersection of robotics engineering and stroke rehabilitation, and will ultimately prepare me to lead a translational research laboratory developing patient-centered approaches to build devices to improve motor control in people with neurological disorders.