# SCH: Model-informed patient-specific rehabilitation using robotics and neuromuscular modeling > **NIH NIH R01** · UNIVERSITY OF DELAWARE · 2022 · $299,971 ## Abstract 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 interve... ## Key facts - **NIH application ID:** 10601240 - **Project number:** 1R01HD111071-01 - **Recipient organization:** UNIVERSITY OF DELAWARE - **Principal Investigator:** Panagiotis Artemiadis - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $299,971 - **Award type:** 1 - **Project period:** 2022-09-21 → 2026-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10601240 ## Citation > US National Institutes of Health, RePORTER application 10601240, SCH: Model-informed patient-specific rehabilitation using robotics and neuromuscular modeling (1R01HD111071-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10601240. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*