CAREER: A Closed-Loop Control Framework for the Treatment of Chronic Stroke

职业：治疗慢性中风的闭环控制框架

• 批准号: 2054191
• 负责人: Eric Wade
• 金额: $ 54.72万
• 依托单位: California Polytechnic State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-12 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054191&HistoricalAwards=false
• 关键词: CAREER; Closed; Loop; Control; Framework

Each year, over 800,000 people suffer from stroke in the United States. Many of these individuals are left with hemiparesis, or weakness on one side of the body. Early after the stroke, this hemiparesis results in pain and reduced strength, rendering task performance difficult. Meanwhile, there may be spontaneous recovery of movement ability and neurological capability. However, due to this early limb weakness, people eventually voluntarily suppress the use of the weaker limb. Thus, a phenomenon emerges known as nonuse: a difference between what people can do and what they choose to do. This is problematic as nonuse is thought to lead to compensating movement strategies, which lead to increased injury and additional medical complications. Nonuse is one example of many phenomena that develop over time as individuals use their own movement strategies when they are not monitored by a clinician. The goal of the current research approach is to automatically measure when people utilize such harmful strategies and to encourage increased limb use through feedback from a digital device. The system is a just-in-time adaptive intervention (JITAI), a technology-based tool capable of determining in real-time when people are performing certain activities and providing them feedback when and where they need it in order to promote recovery. This approach may represent the first of many interventions for people with chronic conditions that develop over time and takes advantage of a well-established scientific principle known as control systems engineering. The approach will model the natural behavior of the human and apply appropriate intervention through the use of control systems strategies. These research goals are closely coupled with educational goals, as control systems engineering can be taught to students at varying levels of academic maturity. As a result, diverse students in both engineering and healthcare will take advantage of summer training programs at the undergraduate level designed to evaluate and simulate the technological approach and through a novel curriculum focused on combining computation and neuroscience at the post-graduate level. Finally, this research will represent a proof of concept for the adaptation of human delivered, evidence-based therapy using automated tools that can be implemented in real world settings for chronic health conditions.The investigator's motivating research theme is ambient assisted living: the use of assistive technologies in real-world settings outside of clinical or laboratory environments to assist people living with disability. Toward this theme, this project focuses on developing and evaluating a framework combining control systems engineering and neurorehabilitation to provide in-home rehabilitation for the chronic stroke population suffering from learned nonuse. The Research Plan presents a novel approach to addressing neurodegenerative disorders (NDs) by treating the symptoms as outputs of a dynamical system. By treating the patient as the 'system' within a control loop, the application of classical control facilitates the use of feedback (to monitor patient symptoms) and a controller (to provide inputs to the patient) to drive symptoms to a desired state. The framework facilitates a methodological and theoretically defensible approach to objective quantification and modeling of disease symptoms, and evidence-based strategies for intervening to mitigate such symptoms. The Research Plan is organized under three aims. The FIRST AIM is to develop a dynamical systems model of stroke system progression. The model includes a Nonuse forward block that describes how behavior results from beliefs about rehabilitation, a Sensorimotor Learning block that describes the relationship between home practice behavior and spontaneous limb use in real world settings and a Nonuse feedback block that relates spontaneous limb use to a person's beliefs about their capability using self-regulation theory. The SECOND AIM is to develop a dynamical systems model of therapy. The model includes a CIMT (constraint induced movement therapy) Transfer Package block that relates beliefs about therapy to the CIMT intervention components (e.g., motivation, forced limb use and positive reinforcement) and a Control-Based Rehabilitation block that uses an SMC (sliding mode control) approach. Noninvasive wearable sensor and functional assessment data obtained from individuals with chronic hemiparetic stroke will be used to calibrate the models of Nonuse and CIMT. The THIRD AIM is to develop and validate the nonlinear control systems-based treatment delivery in participants' homes. Efficacy will be evaluated using the UE-FMA (Upper Extremity Fugi-Meyer Assessment) before and after 4 week studies during which sensors will be worn 6 hours/day.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国，每年有超过80万人患有中风。这些人中的许多人都留下了偏瘫，或身体一侧的弱点。中风后早期，这种轻偏瘫导致疼痛和力量下降，使任务执行困难。同时，运动能力和神经功能可能会自发恢复。然而，由于这种早期的肢体无力，人们最终会自愿抑制使用较弱的肢体。因此，出现了一种被称为“不使用”的现象：人们可以做什么和他们选择做什么之间的差异。这是有问题的，因为不使用被认为会导致补偿运动策略，这会导致损伤增加和其他医疗并发症。不使用是随着时间的推移发展的许多现象的一个例子，因为个体在没有临床医生监测的情况下使用自己的运动策略。目前研究方法的目标是自动测量人们何时使用这种有害策略，并通过数字设备的反馈鼓励增加肢体使用。该系统是一种及时适应性干预（JITAI），这是一种基于技术的工具，能够实时确定人们何时进行某些活动，并在何时何地向他们提供反馈，以促进恢复。这种方法可能代表了对慢性病患者的许多干预措施中的第一种，这些慢性病随着时间的推移而发展，并利用了被称为控制系统工程的成熟科学原理。该方法将模拟人类的自然行为，并通过使用控制系统策略进行适当的干预。这些研究目标与教育目标密切相关，因为控制系统工程可以教授给不同学术成熟度的学生。因此，工程和医疗保健领域的不同学生将利用本科阶段的暑期培训课程，旨在评估和模拟技术方法，并通过一门新的课程，重点是在研究生阶段将计算和神经科学结合起来。最后，这项研究将代表一个概念的证明，为适应人类交付，循证治疗使用自动化工具，可以在真实的世界环境中实施慢性健康conditions.The调查员的激励研究主题是环境辅助生活：辅助技术在临床或实验室环境以外的现实世界环境中的使用，以帮助残疾人生活。 针对这一主题，本项目的重点是开发和评估一个框架相结合的控制系统工程和神经康复提供家庭康复的慢性中风人群患有学习不使用。 该研究计划提出了一种新的方法来解决神经退行性疾病（ND），通过治疗症状作为动力系统的输出。通过将患者视为控制回路内的“系统”，经典控制的应用促进了反馈（以监测患者症状）和控制器（以向患者提供输入）的使用，以将症状驱动到期望的状态。该框架有利于一个方法和理论上站得住脚的方法，以客观量化和建模的疾病症状，并以证据为基础的干预策略，以减轻这些症状。 研究计划是根据三个目标组织的。 第一个目标是建立一个中风系统进展的动力学系统模型。 该模型包括一个Nonuse前向块，描述了如何从信念的行为结果康复，感觉运动学习块，描述了家庭练习行为和自发肢体使用之间的关系，在真实的世界设置和Nonuse反馈块，涉及自发肢体使用一个人的信念，他们的能力，使用自我调节理论。 第二个目标是开发一个治疗的动力系统模型。 该模型包括CIMT（约束诱导运动治疗）转移包块，其将关于治疗的信念与CIMT干预组件（例如，动机，强迫肢体使用和正强化）和基于控制的康复块，使用SMC（滑动模式控制）方法。 从慢性轻偏瘫卒中患者中获得的无创可穿戴传感器和功能评估数据将用于校准Nonuse和CIMT模型。 第三个目标是在参与者家中开发和验证基于非线性控制系统的治疗交付。 在为期4周的研究之前和之后，将使用UE-FMA（Upper Psychiity Fugi-Meyer Assessment）评估传感器的有效性，在此期间，传感器将每天佩戴6小时。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke

• DOI: 10.1109/embc46164.2021.9629885
• 发表时间: 2021-11
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
• 作者: Aaron Miller;Eric Wade