Literature DB >> 11001514

The relationship between electrical stimulus and joint torque: a dynamic model.

M Ferrarin1, A Pedotti.   

Abstract

The knowledge of the behavior of electrically activated muscles is an important requisite for the development of functional electrical stimulation (FES) systems to restore mobility to persons with paralysis. The aim of this work was to develop a model capable of relating electrical parameters to dynamic joint torque for FES applications. The knee extensor muscles, stimulated using surface electrodes, were used for the experimental preparation. Both healthy subjects and people with paraplegia were tested. The dynamics of the lower limb were represented by a nonlinear second order model, which took account of the gravitational and inertial characteristics of the anatomical segments as well as the damping and stiffness properties of the knee joint. The viscous-elastic parameters of the system were identified experimentally through free pendular movements of the leg. Leg movements induced by quadriceps stimulation were acquired too, using a motion analysis system. Results showed that, for the considered experimental conditions, a simple one-pole transfer function is able to model the relationship between stimulus pulsewidth (PW) and active muscle torque. The time constant of the pole was found to depend on the stimulus pattern (ramp or step) while gain was directly dependent on stimulation frequency.

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Year:  2000        PMID: 11001514     DOI: 10.1109/86.867876

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  11 in total

1.  Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury.

Authors:  Mourad Benoussaad; Philippe Poignet; Mitsuhiro Hayashibe; Christine Azevedo-Coste; Charles Fattal; David Guiraud
Journal:  Med Biol Eng Comput       Date:  2013-02-05       Impact factor: 2.602

2.  A Modified Dynamic Surface Controller for Delayed Neuromuscular Electrical Stimulation.

Authors:  Naji Alibeji; Nicholas Kirsch; Brad E Dicianno; Nitin Sharma
Journal:  IEEE ASME Trans Mechatron       Date:  2017-05-16       Impact factor: 5.303

3.  Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh.

Authors:  Stefan Goetz; Joerg Kammermann; Florian Helling; Thomas Weyh; Zhongxi Li
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2022-03-28       Impact factor: 4.528

4.  Methods for Dynamic Characterization of the Major Muscles Activating the Lower Limb Joints in Cycling Motion.

Authors:  Navit Roth; Avi Wiener; Joseph Mizrahi
Journal:  Eur J Transl Myol       Date:  2014-04-02

5.  Development of KIINCE: A kinetic feedback-based robotic environment for study of neuromuscular coordination and rehabilitation of human standing and walking.

Authors:  Wendy L Boehm; Kreg G Gruben
Journal:  J Rehabil Assist Technol Eng       Date:  2018-09-20

6.  Closed-Loop Torque and Kinematic Control of a Hybrid Lower-Limb Exoskeleton for Treadmill Walking.

Authors:  Chen-Hao Chang; Jonathan Casas; Steven W Brose; Victor H Duenas
Journal:  Front Robot AI       Date:  2022-01-20

7.  Development of a mathematical model for predicting electrically elicited quadriceps femoris muscle forces during isovelocity knee joint motion.

Authors:  Ramu Perumal; Anthony S Wexler; Stuart A Binder-Macleod
Journal:  J Neuroeng Rehabil       Date:  2008-12-10       Impact factor: 4.262

8.  Predicting non-isometric fatigue induced by electrical stimulation pulse trains as a function of pulse duration.

Authors:  M Susan Marion; Anthony S Wexler; Maury L Hull
Journal:  J Neuroeng Rehabil       Date:  2013-02-02       Impact factor: 4.262

9.  Control of Dynamic Limb Motion Using Fatigue-Resistant Asynchronous Intrafascicular Multi-Electrode Stimulation.

Authors:  Mitchell A Frankel; V John Mathews; Gregory A Clark; Richard A Normann; Sanford G Meek
Journal:  Front Neurosci       Date:  2016-09-13       Impact factor: 4.677

10.  Neural Network-Based Muscle Torque Estimation Using Mechanomyography During Electrically-Evoked Knee Extension and Standing in Spinal Cord Injury.

Authors:  Muhammad Afiq Dzulkifli; Nur Azah Hamzaid; Glen M Davis; Nazirah Hasnan
Journal:  Front Neurorobot       Date:  2018-08-10       Impact factor: 2.650

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