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.
