Analytical Tools for Optimizing Neurorehabilitation of Gait

优化步态神经康复的分析工具

• 批准号: 7161059
• 负责人: W. Scott Selbie
• 金额: $ 10万
• 依托单位: C-MOTION, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-20 至 2007-03-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7161059
• 关键词: assistive device /technology; bioengineering /biomedical engineering; biofeedback; biomechanics; biomedical equipment development; clinical research; computational neuroscience; data collection methodology /evaluation; functional ability; gait; human data; medical rehabilitation related tag; musculoskeletal regeneration; nervous system disorder therapy; nervous system regeneration; psychomotor function; three dimensional imaging /topography

DESCRIPTION (provided by applicant): Project Summary/Abstract: In recent years, the field of neurological rehabilitation has been reinvigorated with the finding that the central nervous system retains plasticity even into adulthood. Interventions utilizing massed practice neurorehabilitation provide a setting in which an individual with upper motor neuron lesions performs hundreds of repetitions of a behavior per session using the affected extremity(ies); the goal is to develop skill (motor relearning) in the performance of the behavior. In this context, the ability of the spinal cord to reorganize to produce improvements in function appears to be highly sensitive to the appropriate training environment. For example, patients that received body-weight supported treadmill training, following spinal cord injury and stroke, showed improved EMG activation patterns, more natural walking characteristics, and were able to bear more weight on their legs and had higher returns in functional walking ability when compared to patients who received standard physiotherapy. 1 limitation with these gait training protocols is that a number of key training variables are not well controlled for or understood, yet presumably play an instrumental role in functional recovery. For example, walking speed, level of body-weight support, and leg kinematics have all been shown to be important in eliciting and sustaining locomotor patterns in animals, yet we currently lack quantitative techniques for determining how to customize these parameters for individual patients. 1 possible solution to identifying the set of optimal gait training parameters is by integrating active assistance and quantitative assessment that would allow the systematic exploration of walking across various conditions. Recent modifications to the Lokomat (Hocoma, Switzerland), a fully programmable gait trainer, allow us to develop assessment algorithms that make it possible to study peripheral conditions which directly mediate sensory afferent drive to the spinal cord. The specific goal of this Phase I SBIR project is to develop analytical tools for neurorehabilitation of gait for individuals with spinal cord injury or stroke directed at facilitating experiments for optimizing training conditions that promote the highest returns in motor recovery. Our guiding premise is that quantitative tools for assessing motor function will aid both clinical diagnoses and guidance of rehabilitation strategies to improve motor function. We believe that patients with neurological injuries who are trained at conditions that result in the most appropriate joint moments and muscle activation patterns will achieve higher levels of functional recovery than those trained at conditions chosen using heuristic methods. Project Narrative: Rehabilitation from stroke or spinal cord injury is labor-intensive, relying on therapy and assessments that often require direct contact between physical therapist and patient. Physical therapy techniques encouraging correct movement patterns and discouraging incorrect movement patterns have been shown to promote recovery, however, because reimbursement for physical therapy time for stroke patients has decreased substantially robotic devices may be of substantial value for rehabilitation to free therapists from repetitive tasks such as moving a patients' plegic arm to simulate independent reaching, to provide objective, quantitative assessment of motor performance, and to explore the possibility of delivering regular, meaningful therapy independent of the constant attention of the therapist. The specific goal of this Phase I SBIR project is to develop analytical tools for neurorehabilitation of gait for individuals with spinal cord injury or stroke directed at facilitating experiments for optimizing training conditions that promote the highest returns in motor recovery.

描述(申请人提供)：项目摘要/摘要：近年来，神经康复领域重新焕发活力，发现中枢神经系统即使在成年后也保持可塑性。利用大规模实践神经康复的干预措施提供了一种环境，在这种环境中，患有上运动神经元损伤的个人使用受影响的肢体在每个会话中重复数百次行为；目标是发展行为执行中的技能(运动再学习)。在这种情况下，脊髓重组以改善功能的能力似乎对适当的训练环境高度敏感。例如，与接受标准理疗的患者相比，接受体重支持的跑步机训练的患者在脊髓损伤和中风后，表现出更好的肌电激活模式，更自然的行走特征，能够承受更多的腿部重量，功能行走能力方面的回报更高。1这些步态训练方案的局限性是，一些关键的训练变量没有得到很好的控制或理解，但可能在功能恢复中发挥了重要作用。例如，步行速度、体重支持水平和腿部运动学都被证明在诱导和维持动物的运动模式方面都很重要，但我们目前缺乏量化技术来确定如何为个别患者定制这些参数。1确定一组最佳步态训练参数的可能解决方案是将主动辅助和定量评估相结合，从而允许系统地探索跨越各种条件的步行。最近对Lokomat(瑞士霍科马)的完全可编程步态训练器进行了改进，使我们能够开发评估算法，使研究直接介导感觉传入驱动到脊髓的外周条件成为可能。这个第一阶段SBIR项目的具体目标是开发用于脊髓损伤或中风患者的步态神经康复的分析工具，旨在促进实验，以优化训练条件，促进运动恢复的最高回报。我们的指导前提是，评估运动功能的量化工具将有助于临床诊断和指导康复策略，以改善运动功能。我们认为，神经损伤患者在产生最合适的关节瞬间和肌肉激活模式的条件下进行训练，将比在使用启发式方法选择的条件下进行训练的患者获得更高水平的功能恢复。项目简介：中风或脊髓损伤的康复是劳动密集型的，依赖于治疗和评估，这些往往需要物理治疗师和患者之间的直接接触。然而，鼓励正确的运动模式和阻止错误的运动模式的物理治疗技术已被证明可以促进康复，因为中风患者的物理治疗时间补偿大幅减少，因为机器人设备可能对康复具有实质性的价值，使医生从重复性任务中解放出来，例如移动患者的瘫痪手臂以模拟独立伸展，对运动表现提供客观、定量的评估，以及探索提供不依赖治疗师持续关注的定期、有意义的治疗的可能性。这个第一阶段SBIR项目的具体目标是开发用于脊髓损伤或中风患者的步态神经康复的分析工具，旨在促进实验，以优化训练条件，促进运动恢复的最高回报。