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Author Notes:

Correspondence: Benjamin J. Fregly fregly@rice.edu

Reviewed by: Thomas C. Bulea, National Institutes of Health (NIH), United States; Sarah A. Roelker, University of Texas at Austin, United States

NS performed all model personalization and treatment optimization tasks, wrote the first draft of the manuscript, and assisted with preparation of figures and tables.

AM assisted with development of the personalized neuromusculoskeletal model, modification of the optimal control problem formulations to accommodate the goals of the present study, and review of the manuscript draft.

JA oversaw experimental data collection for the project, processed all experimental data, and provided feedback on the manuscript draft.

LT and TK recruited the experimental subject, organized the experimental data collection session, helped identify treatment scenarios to optimize, and provided feedback on the manuscript draft.

BF coordinated the entire project, oversaw model personalization and treatment optimization tasks, edited the manuscript draft, and prepared the final figures and tables.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Research Funding:

This study was funded by National Science Foundation grant CBET 1404767 and by a National Center for Simulation in Rehabilitation Research pilot project award provided through NIH grant P2C HD065690.


  • computational modeling
  • direct collocation optimal control
  • fast treadmill training
  • functional electrical stimulation
  • muscle synergies
  • neuromusculoskeletal modeling
  • paretic propulsion
  • stroke

Computational Design of FastFES Treatment to Improve Propulsive Force Symmetry during Post-stroke Gait: A feasibility study


Journal Title:

Frontiers in Neurorobotics


Volume 13


, Pages 80-80

Type of Work:

Article | Final Publisher PDF


Stroke is a leading cause of long-term disability worldwide and often impairs walking ability. To improve recovery of walking function post-stroke, researchers have investigated the use of treatments such as fast functional electrical stimulation (FastFES). During FastFES treatments, individuals post-stroke walk on a treadmill at their fastest comfortable speed while electrical stimulation is delivered to two muscles of the paretic ankle, ideally to improve paretic leg propulsion and toe clearance. However, muscle selection and stimulation timing are currently standardized based on clinical intuition and a one-size-fits-all approach, which may explain in part why some patients respond to FastFES training while others do not. This study explores how personalized neuromusculoskeletal models could potentially be used to enable individual-specific selection of target muscles and stimulation timing to address unique functional limitations of individual patients post-stroke. Treadmill gait data, including EMG, surface marker positions, and ground reactions, were collected from an individual post-stroke who was a non-responder to FastFES treatment. The patient’s gait data were used to personalize key aspects of a full-body neuromusculoskeletal walking model, including lower-body joint functional axes, lower-body muscle force generating properties, deformable foot-ground contact properties, and paretic and non-paretic leg neural control properties. The personalized model was utilized within a direct collocation optimal control framework to reproduce the patient’s unstimulated treadmill gait data (verification problem) and to generate three stimulated walking predictions that sought to minimize inter-limb propulsive force asymmetry (prediction problems). The three predictions used: (1) Standard muscle selection (gastrocnemius and tibialis anterior) with standard stimulation timing, (2) Standard muscle selection with optimized stimulation timing, and (3) Optimized muscle selection (soleus and semimembranosus) with optimized stimulation timing. Relative to unstimulated walking, the optimal control problems predicted a 41% reduction in propulsive force asymmetry for scenario (1), a 45% reduction for scenario (2), and a 64% reduction for scenario (3), suggesting that non-standard muscle selection may be superior for this patient. Despite these predicted improvements, kinematic symmetry was not noticeably improved for any of the walking predictions. These results suggest that personalized neuromusculoskeletal models may be able to predict personalized FastFES training prescriptions that could improve propulsive force symmetry, though inclusion of kinematic requirements would be necessary to improve kinematic symmetry as well.

Copyright information:

© 2019 Sauder, Meyer, Allen, Ting, Kesar and Fregly.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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