Literature DB >> 29378445

Real-Time Closed-Loop Functional Electrical Stimulation Control of Muscle Activation with Evoked Electromyography Feedback for Spinal Cord Injured Patients.

Zhan Li1,2, David Guiraud2, David Andreu2, Anthony Gelis3, Charles Fattal3,4, Mitsuhiro Hayashibe2,5.   

Abstract

Functional electrical stimulation (FES) is a neuroprosthetic technique to help restore motor function of spinal cord-injured (SCI) patients. Through delivery of electrical pulses to muscles of motor-impaired subjects, FES is able to artificially induce their muscle contractions. Evoked electromyography (eEMG) is used to record such FES-induced electrical muscle activity and presents a form of [Formula: see text]-wave. In order to monitor electrical muscle activity under stimulation and ensure safe stimulation configurations, closed-loop FES control with eEMG feedback is needed to be developed for SCI patients who lose their voluntary muscle contraction ability. This work proposes a closed-loop FES system for real-time control of muscle activation on the triceps surae and tibialis muscle groups through online modulating pulse width (PW) of electrical stimulus. Subject-specific time-variant muscle responses under FES are explicitly reflected by muscle excitation model, which is described by Hammerstein system with its input and output being, respectively, PW and eEMG. Model predictive control is adopted to compute the PW based on muscle excitation model which can online update its parameters. Four muscle activation patterns are provided as desired control references to validate the proposed closed-loop FES control paradigm. Real-time experimental results on three able-bodied subjects and five SCI patients in clinical environment show promising performances of tracking the aforementioned reference muscle activation patterns based on the proposed closed-loop FES control scheme.

Entities:  

Keywords:  Functional electrical stimulation (FES); evoked electromyography (eEMG); muscle activation control; spinal cord injury (SCI)

Mesh:

Year:  2017        PMID: 29378445     DOI: 10.1142/S0129065717500630

Source DB:  PubMed          Journal:  Int J Neural Syst        ISSN: 0129-0657            Impact factor:   5.866


  4 in total

1.  Development of an IoT Electrostimulator with Closed-Loop Control.

Authors:  Túlio Fernandes De Almeida; Luiz Henrique Bertucci Borges; André Felipe Oliveira de Azevedo Dantas
Journal:  Sensors (Basel)       Date:  2022-05-07       Impact factor: 3.847

2.  Combining Action Observation Treatment with a Brain-Computer Interface System: Perspectives on Neurorehabilitation.

Authors:  Fabio Rossi; Federica Savi; Andrea Prestia; Andrea Mongardi; Danilo Demarchi; Giovanni Buccino
Journal:  Sensors (Basel)       Date:  2021-12-20       Impact factor: 3.576

3.  Wireless Electrical Stimulators and Sensors Network for Closed Loop Control in Rehabilitation.

Authors:  David Andreu; Benoît Sijobert; Mickael Toussaint; Charles Fattal; Christine Azevedo-Coste; David Guiraud
Journal:  Front Neurosci       Date:  2020-02-19       Impact factor: 4.677

Review 4.  Advances in neuroprosthetic management of foot drop: a review.

Authors:  Javier Gil-Castillo; Fady Alnajjar; Aikaterini Koutsou; Diego Torricelli; Juan C Moreno
Journal:  J Neuroeng Rehabil       Date:  2020-03-25       Impact factor: 4.262

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.