Modulation of gait dynamics post-stroke

项目总结/摘要 我的职业目标是提高中风步态康复的个性化和开发新颖的康复 使用基于神经力学的数据驱动建模结合假设驱动的技术 实验设计这项拟议中的研究旨在了解神经限制如何影响中风幸存者的生活质量。 协调执行级关节动力学的能力，以灵活调节全身质心（COM） 在行走过程中，慢速倒立摆和快速弹簧质量动力学之间的动力学; 实现稳定、高效、快速的运动。我对单个个体的初步数据表明，COM 动力学是不对称的中风后，但进一步表征联合协调和COM动力学， 需要了解它们与中风后行走中个体间和肢体间差异的关系 功能和治疗反应。描述这些关系的两个主要方法障碍是 缺乏1）对中风后复杂的个体特异性步态动力学进行编码的指标和技术，以及2）长时间 时间序列数据集包含测试神经力学假设所需的各种运动模式， 步态为了应对这些挑战，我将与申办者Ting和共同申办者Berman合作，扩展数据- 在我的博士研究中开发的驱动技术，以识别COM动态并表征其 与关节动力学的关系;与赞助商Ting和共同赞助商Kesar一起，我将设计和收集新的数据集 从中风幸存者的不同运动模式使用生物反馈在步行。目标1：测试是否 中风后COM动力学的个体间和肢体间差异与步行速度相关。我会 评估基线卒中后COM动力学与健全（AB）成人和轻瘫患者之间的相似性。 和非麻痹肢体。此外，我将研究paretic-limb是否从倒立摆过渡到 弹簧质量动力学在更快的跑步机速度，减少COM动力学的不对称性。目标2：表征 降低中风幸存者调节关节动力学以实现所需COM动力学的能力。使用visual 生物反馈处方COM动力学，我将测试中风幸存者是否有能力降低模仿 COM动力学与AB成人相比，并确定关节动力学是否表征COM动力学 在中风幸存者中的准确性高于AB型成人。目标3：测试具有相似COM的个体的子组是否 并且联合动力学比离散度量更准确地预测生物反馈响应。我将测试， 在一系列生物反馈规定的COM动力学中，基线COM和关节动力学可以分类 生物反馈的关节动力学变化比离散的临床或生物力学变量更准确。我 还将通过参加赞助商Kesar正在进行的步态康复研究进行临床试验培训。的 建议的研究和培训将补充我的博士技能，使我能够领导独立的研究 结合实验设计和计算技术来发现神经力学机制 潜在的步态功能障碍，并开发新的步态康复与神经损伤的个人。

PROJECT SUMMARY/ABSTRACT My career goal is to improve the personalization of stroke gait rehabilitation and develop novel rehabilitation technologies using neuromechanics-based data-driven modeling in conjunction with hypothesis-driven experimental design. The proposed research aims to understand how neural constraints impact stroke survivors’ ability to coordinate execution-level joint dynamics to flexibly modulate whole-body center-of-mass (COM) dynamics between slow inverted pendulum and fast spring-mass dynamics during walking; a task critical to achieving stable, efficient, and rapid movement. My preliminary data in a single individual suggest that COM dynamics are asymmetric post-stroke, but further characterization of joint coordination and COM dynamics is needed to understand their relationship with interindividual and inter-limb differences in post-stroke walking function and treatment responses. Two major methodological barriers to characterizing these relationships are a lack of 1) metrics and techniques to encode complex, individual-specific gait dynamics post-stroke, and 2) long time-series datasets containing diverse movement patterns needed to test neuromechanical hypotheses about gait. To address these challenges, I will work with Sponsor Ting and Co-Sponsor Berman to extend data- driven techniques developed in my doctoral research to identify COM dynamics and characterize their relationships to joint dynamics; with Sponsor Ting and Co-Sponsor Kesar I will design and collect new datasets from stroke survivors of diverse movement patterns using biofeedback during walking. Aim 1: Test whether interindividual and inter-limb differences in COM dynamics post-stroke are associated with walking speed. I will evaluate the similarity of baseline post-stroke COM dynamics to able-bodied (AB) adults and between paretic and non-paretic limbs. Further, I will examine whether the paretic-limb transitions from inverted pendulum to spring-mass dynamics at faster treadmill speeds, decreasing asymmetry in COM dynamics. Aim 2: Characterize reductions in stroke survivors’ ability to modulate joint dynamics to achieve desired COM dynamics. Using visual biofeedback to prescribe COM dynamics, I will test whether stroke survivors have reduced ability to emulate COM dynamics compared to AB adults and determine if joint dynamics characterize COM dynamics less accurately in stroke survivors than AB adults. Aim 3: Test whether sub-groups of individuals with similar COM and joint dynamics predict biofeedback responses more accurately than discrete metrics. I will test whether, across a range of biofeedback-prescribed COM dynamics, baseline COM and joint dynamics can classify changes in joint dynamics with biofeedback more accurately than discrete clinical or biomechanical variables. I will also have training in clinical trials by participating in Sponsor Kesar’s ongoing gait rehabilitation study. The proposed research and training will complement my doctoral skillset, preparing me to lead independent research combining experimental design and computational techniques to discover neuromechanical mechanisms underlying gait dysfunction and develop novel gait rehabilitation for individuals with neurological injuries.