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

使用机器人手矫形器进行生物反馈和特定任务训练对中风后康复随意肌调节的影响

• 批准号: 10751274
• 负责人: Ava Chen
• 金额: $ 4.77万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751274
• 关键词: Address; Adoption; Algorithms; Area; Biofeedback; Biofeedback Training; Chronic; Clinic; Collaborations; Complement; Complication; Data; Detection; Development Plans; Devices; Discrimination; Electromyography; Engineering; Exercise; Extensor; Feedback; Flexor; Forearm; Generations; Goals; Hand; Human; Impairment; Improve Access; Individual; Intervention; Intuition; Laboratories; Laboratory Research; Lifting; Limb structure; Measures; Mediating; Mentors; Methods; Monitor; Motor; Movement; Muscle; Muscle Contraction; Neurologic; Neurological outcome; Occupational Therapist; Orthotic Devices; Outcome; Paresis; Patients; Pattern; Perception; Performance; Persons; Physical therapy; Process; Protocols documentation; Psychological reinforcement; Quality of life; Regimen; Rehabilitation therapy; Research; Robot; Robotics; Series; Signal Transduction; Skeletal Muscle; Stroke; Surface; System; Techniques; Testing; Therapeutic; Therapeutic Effect; Time; Training; Translational Research; Visual; Work; arm; arm paresis; chronic stroke; clinical efficacy; electromyographic biofeedback; exoskeleton; experimental study; fighting; functional assistance; functional outcomes; grasp; hand dysfunction; hand rehabilitation; hand therapy; hemiparesis; improved; insight; interest; mobile computing; motor control; motor learning; motor skill learning; movement practice; muscle stiffness; nervous system disorder; neuromuscular; new technology; patient oriented; physical therapist; post intervention; post stroke; recruit; response; robot assistance; robot control; robot exoskeleton; robotic device; robotic system; robotic training; sensor; spasticity; stroke hemiparesis; stroke rehabilitation; stroke survivor; synergism; therapy outcome; upper limb hemiparesis

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.

项目摘要 用于手部康复的机器人设备在改善运动训练和鼓励运动方面显示出希望。 受损肢体的功能性使用。这些设备可以为日常活动提供帮助， 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， 他们可以提供超越诊所或实验室范围的治疗。我们使用表面肌电图 (EMG)传感器和意图检测算法使中风后轻偏瘫患者能够直观地 控制一个可穿戴的机器人手部矫形器。然而，采用该设备或类似设备的主要障碍是 过度痉挛，这是放大了用户的招聘时，所有可用的肌肉施加努力， 控制机器人。当尝试运动模式时，这种肌肉的过度共同激活是常见的。 中风并发症我们的工作旨在解决这个问题，通过使用肌电生物反馈，显示真实的- 向用户提供关于肌肉激活的时间信息，以共同训练人类-机器人系统来生成运动模式 以最大限度地减少过度共激活。受肌肉活动视觉生物反馈研究的启发， 这些研究揭示了康复训练的良好效果，我们对慢性中风受试者的初步研究 已经表明，有些人保留了一些改变肌肉激活模式的能力，以应对 肌电生物反馈。这项研究的目的是确定是否可以利用肌电生物反馈， 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共激活和痉挛。这一目标将通过以下方式实现： 两个目标。Aim 1采用生物反馈和机器人训练的辅助方法。我们将确定 中风幸存者使用机器人EMG生物反馈时可实现屈肌/伸肌解耦 援助.我们希望肌电生物反馈有助于识别和产生运动模式，从而导致 最小异常共激活。在目标1中，受试者将参加单次实验， 机器人辅助的手部运动与协调的屈肌/伸肌激活对准。目标2需要 康复的方法，并将调查是否多会话的做法与肌电图生物反馈和机器人 训练产生康复效果和功能结果，这些效果和功能结果在矫形器被移除之后仍然存在。到 为了实现这一目标，我们将进行多阶段的训练方案，其中矫形器需要逐步 更高保真的激活信号，以帮助运动完成。拟议的项目将提供见解 了解训练期间人机流畅性的进展以及训练后对运动学习的更好理解 中风此培训补充了我的发展计划，提供了与 跨学科的指导和合作团队，以追求在机器人工程的交叉点的项目 和中风康复，并将最终准备我领导一个转化研究实验室开发 以患者为中心的方法来构建设备，以改善神经系统疾病患者的运动控制。