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Use of a Foot-Induced Digitally Controlled Resistance Device for Functional Magnetic Resonance Imaging Evaluation in Patients with Foot Paresis

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Real-time controller for foot-drop correction by using surface electromyography sensor.

Yousif I Al Mashhadany¹, Nasrudin Abd Rahim

¹Department of Electrical Engineering, College of Engineering, University of Anbar, Baghdad, Iraq.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine

|May 3, 2013

Summary

This study presents a novel controller for foot drop, utilizing surface electromyography signals and artificial neural networks to predict joint angles and control leg muscle stimulation for improved walking.

Keywords:

Surface electromyography artificial neural network foot-drop correction

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Published on: April 12, 2016

Area of Science:

Biomedical Engineering
Neuroscience
Rehabilitation Technology

Background:

Foot drop, caused by muscle paralysis, impairs walking due to nerve signal disruption affecting heel strike.
Lesions in the brain, spinal cord, or peripheral nerves can lead to foot drop, resulting in a dorsiflexed foot.
Existing assistive devices often lack sophisticated control mechanisms for dynamic gait restoration.

Purpose of the Study:

To design and analyze a novel controller for individuals with foot drop.
To utilize surface electromyography (sEMG) signals for accurate joint angle estimation.
To develop an artificial neural network (ANN) based system for predicting muscle stimulation to restore ankle function.

Main Methods:

sEMG signals were acquired from human leg muscles during various-speed movements.
sEMG signals underwent filtering, amplification, and normalization.
Extracted sEMG parameters trained an ANN for predicting knee and ankle joint angles.
A second ANN phase estimated control signals for targeted muscle stimulation.

Main Results:

The ANN accurately predicted joint angles based on processed sEMG signals.
The system demonstrated feasibility in estimating the required muscle stimulation for ankle movement.
Simulations in MATLAB/Simulink confirmed the controller's effectiveness.

Conclusions:

The developed controller, integrating sEMG and ANN, shows promise for addressing foot drop.
This approach offers a potential pathway for restoring natural walking patterns in affected individuals.
Further clinical validation is warranted to confirm real-world efficacy and patient benefit.