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.

项目概要 用于手部康复的机器人设备有望改善运动训练和鼓励的机会 这些设备可以为日常活动提供帮助并增强受损肢体的功能使用。 传统的康复方法是一个特别令人兴奋的研究领域，因为 他们可以提供超出诊所或实验室范围的治疗。我们使用表面肌电图。 （肌电图）传感器和意图检测算法使中风后偏瘫患者能够直观地 控制可穿戴机器人手矫形器然而，采用这种或类似设备的主要障碍是。 过度痉挛，当用户用力做动作时，会募集所有可用的肌肉，从而加剧痉挛 尝试运动模式时肌肉的过度协同激活是一种常见现象。 我们的工作旨在通过使用肌电图生物反馈（显示真实的中风并发症）来解决这个问题。 向用户提供有关肌肉激活的时间信息，共同训练人机系统以生成运动模式 受到肌肉活动视觉生物反馈研究的启发， 我们对慢性中风受试者的初步工作在康复训练方面取得了可喜的成果 已经表明，一些人保留了一些改变肌肉激活模式的能力，以响应 肌电图生物反馈本研究的目的是确定肌电图生物反馈是否可以用于治疗。 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追求来实现。 目标 1 采用生物反馈和机器人训练的辅助方法。 当中风幸存者使用肌电图生物反馈和机器人时可以实现屈肌/伸肌解耦 我们期望肌电图生物反馈能够帮助辨别和产生运动模式，从而导致 在目标 1 中，受试者将参加单次强化实验。 机器人辅助手部运动与协调的屈肌/伸肌激活一致。 康复方法，并将研究是否使用肌电图生物反馈和机器人进行多次练习 训练产生的康复效果和功能结果在矫形器移除后仍然持续存在。 为了实现这一目标，我们将进行多阶段训练方案，其中矫形器需要逐步 更高保真度的激活信号以帮助完成运动。拟议的项目将提供见解。 在训练过程中提高人机流畅性，并在训练后更好地理解运动学习 该培训通过提供与中风一起工作的机会来补充我的发展计划。 跨学科指导和协作团队致力于机器人工程交叉项目 和中风康复，并最终让我准备好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。