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（由申请人提供）：项目摘要/摘要：近年来，随着中枢神经系统即使到成年仍保持可塑性的发现，神经康复领域重新焕发了活力。利用大规模实践神经康复的干预提供了一种环境，其中患有上运动神经元损伤的个体使用受影响的肢体在每次会话中执行数百次重复的行为;目标是在行为的表现中发展技能（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，与接受标准物理治疗的患者相比，接受体重支持跑步机训练的患者在脊髓损伤和中风后表现出改善的EMG激活模式，更自然的行走特征，并且能够在腿部承受更多重量，功能性行走能力的恢复更高。这些步态训练方案的局限性在于，许多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起着重要作用。例如，步行速度、体重支撑水平和腿部运动学都已被证明在动物中引发和维持运动模式方面很重要，但我们目前缺乏定量技术来确定如何为个体患者定制这些参数。识别一组最佳步态训练参数的一种可能的解决方案是通过集成主动辅助和定量评估，这将允许系统地探索各种条件下的行走。最近修改的Lokomat（Hocoma，瑞士），一个完全可编程的步态训练器，使我们能够开发评估算法，使其能够研究直接介导感觉传入驱动到脊髓的外周条件。这个第一阶段SBIR项目的具体目标是为脊髓损伤或中风患者的步态神经康复开发分析工具，旨在促进优化训练条件的实验，以促进运动恢复的最高回报。我们的指导前提是，定量评估运动功能的工具将有助于临床诊断和指导康复策略，以改善运动功能。我们相信，在最合适的关节力矩和肌肉激活模式的条件下训练的神经损伤患者，将比在使用启发式方法选择的条件下训练的患者实现更高水平的功能恢复。项目叙述：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，通常需要物理治疗师和患者之间的直接接触。鼓励正确的运动模式和阻止不正确的运动模式的物理治疗技术已经被证明可以促进恢复，然而，因为对中风患者的物理治疗时间的补偿已经大大减少，机器人设备可能对康复具有实质性的价值，以使治疗师从重复的任务中解放出来，例如移动患者的瘫痪手臂以模拟独立的伸展，以提供客观的，运动表现的定量评估，并探索提供定期的，有意义的治疗独立于治疗师的持续关注的可能性。这个第一阶段SBIR项目的具体目标是为脊髓损伤或中风患者的步态神经康复开发分析工具，旨在促进优化训练条件的实验，以促进运动恢复的最高回报。