SCH: Model-informed patient-specific rehabilitation using robotics and neuromuscular modeling

项目描述 1动机 中风是美国长期残疾的主要原因。中风幸存者现在构成 20岁以上人群中约有3%，50%的中风受试者的推进力受损 在麻痹侧，导致不对称运动和平衡受损[1]。偏瘫患者 在许多中风后个体中观察到的步态比健康人更慢且代谢更昂贵。 个人[2-6]，是减少社区参与和生活质量的主要因素[7-11]。 当代的步态训练方法是基于经常在跑步机上进行的重复治疗[12]， 其变体包括人类或机器人辅助[13]、体重支撑[14]和 功能性电刺激[15]。 机器人干预能够系统和准确地调节关节水平变量，如阿西斯- 转矩和关节角度/速度。机器人技术是步态训练的一个有趣的工具，但能力 使用机器人作为工具，以支持运动学习康复的目的还没有完全 演示。机器人辅助步态康复的早期实施提供了不令人信服的或否定的， 有效结果[13，16]，如荟萃分析[17]中广泛量化的艾德。目前，机器人辅助的效果 中风患者的步态训练尚未超过传统治疗方法[17]。 我们推测，这种限制主要归因于用于机器人辅助步态训练的控制器- ing.大多数专门设计用于步态康复的机器人设备都使用各种 控制器形式（例如，力控制、位置控制或阻抗控制）以及控制器更新方法 (e.g.,按需辅助控制、肢体间协调或有限状态机），以最终促进spe- [2018 - 12 - 18]第18话这些方法的有限效率可能是由于它们缺乏 针对步态的特定功能机制，这些机制仅部分由关节运动学描述。 从一个极端简化主义的角度来看，步行是推动一个人的质心在一个理想的方向 而不是下降。从根本上说，步行涉及三个主要的子任务：推进，肢体前移， 平衡[19]。在这些组成部分中，肢体前移可能是基于运动学控制的，但是是最少的 精力旺盛的要求。相反，推进和平衡的子任务需要精确的神经肌肉 协调，特别是协调步行者和地面之间的相互作用力。尽管 他们的根本重要性，有很少的努力，康复机器人在发展中 研究和/或训练中风后康复中的推进力和平衡的机器人辅助方法。 拟议研究的总体目标 测量 是为了推进治疗工程学的科学 通过识别最佳机器人干预来实现步态“。 和具有特定功能结果的治疗。Stiffne.ss扰动模型 这些干预措施将使用新的 目标增强推进系统的模拟方法评估 中风幸存者的平衡能力感官计划 机器人中的行为范式将应用于一个独特的 机器人辅助模型知情康复的方法- 的研究。拟议的框架将包括- 光栅机器人解决方案，将允许创建 感觉运动机制的综合模型 步态不稳。然后，这些模型将通知一组 对中风幸存者的干预， 其中的一部分将被反馈到开发的模型中， 发现并提出新的患者特异性训练- 战略。拟议的方法将使图1：拟议的综合研究框架如下： 更好地理解机器人中的感觉-计划-行动范式的基本机制。 负责行走，并导致设计 优化和个性化的卒中后康复策略。建议的总体框架 49

PROJECT DESCRIPTION 1 Motivation Stroke is a leading cause of long-term disability in the United States. Stroke survivors now constitute around 3% of the over-20 population, with 50% of stroke-affected subjects left with impaired propulsion on the paretic side, resulting in asymmetric movement and compromised balance [1]. The hemiparetic gait observed in many individuals post-stroke is slower and more metabolically expensive than in healthy individuals [2–6], and is a primary contributor to reduced community participation and quality of life [7–11]. Contemporary approaches to gait training are based on repetitive therapy often conducted on treadmills [12], with variants including the combination of human or robotic assistance [13], body weight support [14], and functional electrical stimulation [15]. Robotic intervention enables systematic and accurate modulation of joint-level variables, such as assis- tance torques and joint angles/velocities. Robotics is an intriguing tool for gait training, but the capability of using robots as tools to support locomotor learning for rehabilitation purposes has not yet been fully demonstrated. Earlier implementations of robot-aided gait rehabilitation provided non-convincing or nega- tive results [13, 16], as extensively quantified in a meta-analysis [17]. Currently, the effects of robot-aided gait training in stroke have yet to exceed those achieved with conventional therapy methods [17]. We speculate that such limitations are mostly imputable to the controllers used for robot-aided gait train- ing. The majority of robotic devices, designed specifically to rehabilitate gait, utilize one of the various controller forms (e.g., force control, position control, or impedance control), and controller update methods (e.g., assist-as-needed control, inter-limb coordination, or finite state machine), to ultimately promote spe- cific features of gait kinematics [18]. The limited efficacy of these methods could be due to their lack of targeting specific functional mechanisms of gait, which are only partially described by joint kinematics. From an extremely reductionist perspective, walking is pushing ones' center of mass in a desired direction while not falling. Fundamentally, walking involves three main sub-tasks: propulsion, limb advancement, and balance [19]. Of these components, limb advancement may be based on kinematic control, but is the least energetically demanding. Instead, the sub-tasks of propulsion and balance require precise neuromuscular coordination, and specifically mediation of the interaction forces between the walker and ground. Despite their fundamental importance, there have been very little efforts in rehabilitation robotics in developing robot-aided methods to study and/or train propulsion and balance in post-stroke rehabilitation. The overarching goal of the proposed research Measure is to advance the science of therapeutic engineering Walking Surfac~ for gait by identifying optimal robot interventions " ., and therapies with specific functional outcomes. Stiffne.ss Perturbations Model Those interventions will be developed using a new modeling approach to target enhanced propulsion Evaluate and balance in stroke survivors. The sense-plan- act paradigm in robotics will be applied in a unique way to robot-assisted model-informed rehabilita- tion research. The proposed framework will inte- grate robotic solutions that will allow the creation of comprehensive models of sensorimotor mecha- nisms of gait. These models will then inform a set of interventions to stroke survivors, the outcomes of which will be fed back to the developed models to uncover and suggest novel patient-specific train- ing strategies. The proposed approach will enable Figure 1: Proposed integrative research framework fol- a better understanding of essential mechanisms lowing the sense-plan-act paradigm in robotics. responsible for walking and lead to the design of optimized and personalized post-stroke rehabilitation strategies. The overall framework of the proposed 49