Cognitive-based Rehabilitation Platform of Hand Grasp after Spinal Cord Injury using Virtual Reality and Instrumented Wearables

项目总结 颈椎水平的脊髓损伤(SCI)会损害手的功能和与环境接触的能力。AS 结果，进行日常生活活动的能力可能会严重受损，直接导致减少 生活质量。物理治疗仍是恢复脊髓损伤后功能的主要途径。体能 康复过程需要参与者的承诺才能在功能上取得有意义的进展。 认知上有吸引力的康复方法可以促进对实践和 改进了动作学习。我们建议开发利用虚拟现实(VR)和 在脊髓损伤后手握力和伸展运动学习过程中增强认知因素的仪表化可穿戴设备。 这些因素包括更强的代理感，或控制感，以及多感官反馈。感觉 与更好的运动控制和各种感觉反馈方式(视觉、听觉、 和触觉)在运动训练中被证明是有效的。然而，这些因素在 传统的理疗方法。我们开发了两个新的基于认知的平台来 康复抓取功能。我们建议在患有慢性脊髓损伤的退伍军人中测试每个平台 宫颈水平。在目标1中，我们将研究我们的第一个平台--“认知”手套--如何改善功能 抓牢了。这只手套包括力和挠性传感器，它们为机器学习算法提供输入，训练成 预测何时实现安全抓取。该手套通过车载感应器提醒使用者安全抓握 提供视觉(LED)、音频(蜂鸣器)和触觉(振动器)反馈的模块。在培训期间，反馈是 以逐渐较短的时间间隔提供，以基于神经科学原理逐步诱导代理 “故意捆绑”的罪名。这一原则表明，有了更大的代理，一个人就能感觉到他们的行动(即，安全 抓握)在时间上更多地与感官结果(即手套反馈)相关联。我们的手套是为用户准备的，而且 现在与定制的VR应用程序兼容，通过参与提供增强的感觉反馈 以及定制的视觉和声音警报。我们假设虚拟现实中增强的反馈将产生甚至 在抓取性能方面比单独使用车载反馈有更大的改善。在目标2中，我们调查了 患有脊髓损伤的退伍军人可能会在虚拟伸展过程中学到更好的手臂肌肉控制。我们已经发展出一种“感官” 为一只手臂提供等长阻力以引发肌电(EMG)模式的支架，可以驱动 虚拟手臂。该人从VR接收视觉反馈，从支架接收肌腱触觉反馈 在训练期间。肌腱刺激可以引起调节肌肉激活模式的运动感。 VR反馈将提供有意识的运动训练提示，而振动反馈将在潜意识中提供 基于聚类分析得出更明显的肌电模式。我们假设不同肌电的促进 通过最大化群集间距离实现的模式将提高触摸式任务的性能。 抓取任务和达到任务在每个目标中的表现都是根据描述 移动的时间、效率和准确性。对于未来的优点建议，我们打算将这些 培训平台以更好地操作动力辅助设备，包括混合动力(电机动力和功能性 电刺激肌肉)手的外骨骼。重要的是，加强认知的概念 使用可穿戴技术、多感官反馈和虚拟现实在 体育训练将适用于所有形式的神经肌肉损伤，包括中风和创伤性脑损伤 除脊髓损伤外，还有其他损伤。我们的团队处于非常有利的地位，可以在开发、翻译和 应用这一技术方法，与康复研究和发展处的 目标。

PROJECT SUMMARY Spinal cord injury (SCI) at the cervical level impairs hand function and the ability to engage the environment. As a result, the ability to perform activities of daily living can be severely compromised, directly leading to reduced quality of life. Physical therapy is still the primary pathway to restore functional abilities after SCI. The physical rehabilitation process requires commitment by the participant to achieve meaningful gains in function. Rehabilitation approaches that are cognitively engaging can facilitate greater commitment to practice and improved movement learning. We propose to develop innovative platforms that utilize virtual reality (VR) and instrumented wearables that enhance cognitive factors during motor learning of hand grasp and reach after SCI. These factors include greater sense of agency, or perception of control, and multi-sensory feedback. Sense of agency is implicated with greater movement control, and various sensory feedback modalities (visual, audio, and haptic) are proven effective in movement training. However, these factors are not well considered in traditional physical therapy approaches. We have developed two novel cognitive-based platforms for rehabilitating grasp and reach function. We propose to test each platform in Veterans with chronic SCI at the cervical level. In Aim 1, we will investigate how our first platform, the “cognition” glove, may improve functional grasp. This glove includes force and flex sensors that provide inputs to a machine learning algorithm trained to predict when secure grasp is achieved. The glove alerts the user of secure grasp through onboard sensory modules providing visual (LED), audio (beeper), and tactile (vibrator) feedback. During training, feedback is provided at gradually shorter time-intervals to progressively induce agency based on the neuroscience principle of ‘intentional binding’. This principle suggests that with greater agency, one perceives their action (i.e., secure grasp) is more coupled in time to a sensory consequence (i.e., glove feedback). Our glove is user-ready, and now has compatibility with customized VR applications to provide enhanced sensory feedback through engaging and customized visual and sound alerts. We hypothesize that enhanced feedback in VR will produce even greater improvements in grasp performance than onboard feedback alone. In Aim 2, we investigate how Veterans with SCI may learn greater arm muscle control during virtual reaching. We have developed a “sensory” brace that provides isometric resistance to one arm to elicit electromyography (EMG) patterns that can drive a virtual arm. The person receives visual feedback from VR and muscle tendon haptic feedback from the brace during training. Tendon stimulation can elicit movement sensations that modulate muscle activation patterns. The VR feedback will provide conscious movement training cues while vibration feedback will subconsciously elicit more distinct EMG patterns based on cluster analysis. We hypothesize that the promotion of distinct EMG patterns, achieved by maximizing inter-cluster distances, will improve performance of a reach-to-touch task. Performance of both the grasp and reach task in each Aim is assessed according to metrics that describe the timing, efficiency, and accuracy of movement. For a future MERIT proposal, we intend to integrate these platforms to train better operation of powered assistive devices, including a hybrid (motor power and functional electrical stimulation of muscle) exoskeleton of the hand. Importantly, the concept of strengthening cognitive agency and learning of movement using wearable technology, multi-sensory feedback, and virtual reality during physical training will be applicable to all forms of neuromuscular impairment, including stroke and traumatic brain injury in addition to SCI. Our team is ideally positioned to take the next steps in developing, translating, and applying this technological approach, which matches the Rehabilitation Research and Development Service’s goals.