This study will compare the performance of a novel data-driven model-predictive controller (MPC) based functional electrical stimulation (FES) system versus a conventional FES system for footdrop correction during treadmill and overground walking tasks in people post-stroke.

Functional electrical stimulation (FES) is a common rehabilitation tool that incorporates electrical stimulation timed with a functional task to augment paretic muscle function in people with neuro-pathologies such as stroke and spinal cord injury. The rigor of previous research has established the safety, as well as both neuro-prosthetic and therapeutic effects of FES systems for standing, walking, and grasping. Stroke is the leading cause of disability, and footdrop is a highly prevalent post-stroke gait deficit, leading to insufficient ankle dorsiflexion during the swing phase of gait, and contributing to reduced mobility. FES systems that correct footdrop to improve gait function and reduce fall risk are gaining popularity, with commercial systems such as enhancing translation potential. Despite their promising functional value, accessibility, and positive neuroplasticity effects, current FES systems have some fundamental limitations, which limit their clinical prescription.

The goal of this project is to overcome two major limitations and technical gaps in FES: rapid onset of muscle fatigue during FES and lack of sophisticated closed-loop control of FES intensity. Most existing FES systems do not automatically modulate stimulation intensity in response to muscle fatigue, and may overstimulate the muscles if fixed (open-loop) stimulation or a pure feedback-based stimulation strategy is used to control FES intensity. To address this limitation, the researchers aim to develop and clinically test FES for improving stroke gait using data-driven FES control systems.

Footdrop is a highly prevalent post-stroke gait deficit, leading to insufficient ankle dorsiflexion during the swing phase of gait, and reducing functional mobility. FES, which is an external application of stimulation to generate muscle contractions during a functional motor task, can achieve muscle force demands during standing and walking, and help persons with stroke and spinal cord injury recover mobility. FES for the correction of footdrop is one of the most popular gait applications of FES, which has been shown to improve mobility and reduce falls.

Although FES has positive effects on walking function, elicits active muscle contractions, and enhances corticomotor excitability, FES is not used as commonly as passive orthotics. Most current FES systems incorporate motion sensors to control the timing of FES during the gait cycle (paretic leg swing phase). However, none of these systems provide automatic closed-loop control of FES intensity, so that optimal stimulation can be delivered for each step, preventing over-stimulation, reducing fatigue, and maintaining optimal muscle performance for a greater number of steps. Additionally, rapid onset of muscle fatigue during FES is caused by synchronous, non-selective, repeated recruitment of largely fatigable muscle fibers.

The researchers will implement an innovative model-predictive controller (MPC) combined with real-time ultrasound-based feedback to deliver optimal FES intensities and minimize fatigue.

Inclusion Criteria:

• >6 months since stroke
• cortical or subcortical stroke
• able to walk 10-meters with or without an assistive device
• sufficient cardiovascular health and ankle stability to walk on treadmill without ankle orthosis
• passive ankle range of motion to benefit from dorsiflexor FES assistance
• resting heart rate 40-100 bpm

Exclusion Criteria:

• cerebellar signs
• score >1 on question 1b (does not know the current month and age) and >0 on question 1c (can not blink eyes and squeeze hands) on NIH Stroke Scale
• inability to communicate with investigators
• neglect/hemianopia
• unexplained dizziness in past 6 months
• sensory loss in paretic leg
• musculoskeletal or medical conditions limiting walking
• neurologic diagnoses other than stroke

De-identified individual participant data for primary dependent variables that underlie the results reported in publications from this study (such as text, tables, appendices) will be made available for sharing with other researchers.

Data will be made available for sharing beginning 9 months and for 2 years after publication of the manuscript presenting the study primary results.

Data will be made available for sharing with researchers who provide a methodologically sound proposal to achieve the aims proposed by the requestor. Proposed purposes for using the shared data include replication studies, meta-analyses or systematic reviews, and other special requests. Researchers wanting to use data from this study should direct their requests to tkesar@emory.edu. To gain access, data requestors will need to sign a data access agreement.