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

在美国，每年有超过80万人患有中风。这些人中的许多人都会留下偏瘫，或身体一侧的虚弱。中风后早期，这种偏瘫会导致疼痛和力量下降，使任务执行变得困难。同时，运动能力和神经功能也可能自发恢复。然而，由于这种早期肢体无力，人们最终自愿抑制使用较弱的肢体。因此，出现了一种被称为无用的现象：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代偿运动策略，这会导致更多的伤害和额外的医疗并发症。不使用是许多现象的一个例子，这些现象随着时间的推移而发展，因为个人在没有临床医生监控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的策略，并通过数字设备的反馈来鼓励更多的肢体使用。该系统是一种即时自适应干预(JITAI)，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，并在他们需要的时间和地点向他们提供反馈，以促进恢复。这种方法可能代表着对慢性病患者的许多干预措施中的第一个，这些慢性病随着时间的推移而发展，并利用了一种被称为控制系统工程的成熟的科学原理。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，工程学和医疗保健的不同学生将利用本科水平的暑期培训计划，以及研究生水平的专注于结合计算和神经科学的新型课程，这些计划旨在评估和模拟技术方法。最后，这项研究将代表对使用自动化工具进行人类提供的循证治疗的概念验证，这些工具可以在现实世界的慢性健康状况下实施。研究人员的激励性研究主题是环境辅助生活：在临床或实验室环境之外的现实世界环境中使用辅助技术来帮助残疾人。围绕这一主题，本项目侧重于开发和评估一个结合控制系统工程和神经康复的框架，为患有习得性不使用的慢性中风人群提供家庭康复。该研究计划提出了一种新的方法，通过将症状作为动力系统的输出来处理神经退行性疾病(NDS)。通过将患者视为控制回路中的“系统”，经典控制的应用促进了反馈(用于监控患者症状)和控制器(用于向患者提供输入)的使用，以将症状驱动到所需的状态。该框架促进了对疾病症状的客观量化和建模的方法论和理论上可辩护的方法，以及减轻这些症状的干预措施的循证战略。研究计划是在三个目标下组织的。第一个目标是建立卒中系统进展的动力学系统模型。该模型包括一个描述康复信念如何导致行为结果的非使用前向块，一个描述家庭练习行为与现实世界环境中自发肢体使用之间关系的感觉运动学习块，以及一个将自发肢体使用与一个人使用自我调节理论的能力信念联系起来的非使用反馈块。第二个目标是开发治疗的动力系统模型。该模型包括将关于治疗的信念与CIMT干预组件(例如，动机、强迫肢体使用和积极强化)联系起来的CIMT(约束性诱导运动疗法)传输包块，以及使用SMC(滑动模式控制)方法的基于控制的康复块。从慢性偏瘫患者身上获得的非侵入性可穿戴传感器和功能评估数据将被用于校准非使用和CIMT模型。第三个目标是开发和验证基于非线性控制系统的参与者家庭治疗服务。在为期4周的研究之前和之后，将使用UE-FMA(上肢Fugi-Meyer评估)对疗效进行评估，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，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.