EMGD-FE: an open source graphical user interface for estimating isometric muscle forces in the lower limb using an EMG-driven model.

Biomed Eng Online

Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering, Biomedical Engineering Program (PEB/COPPE), Federal University of Rio de Janeiro, Av, Horácio Macedo 2030, Bloco H-338, 21941-914 Rio de Janeiro, RJ, Brazil.

Published: April 2014

AI Article Synopsis

  • The paper introduces the "EMG Driven Force Estimator (EMGD-FE)," a MATLAB GUI that estimates muscle forces using EMG signals by simulating muscle dynamics through numerical integration of differential equations.
  • The current version focuses on lower limb muscles during isometric contractions, requiring EMG data collection alongside torque measurements for accuracy.
  • Results demonstrate the application’s capability to estimate muscle forces, exemplified through the quadriceps femoris, while highlighting that estimation accuracy is influenced by various factors, including data collection methods and modeling assumptions.

Article Abstract

Background: This paper describes the "EMG Driven Force Estimator (EMGD-FE)", a Matlab® graphical user interface (GUI) application that estimates skeletal muscle forces from electromyography (EMG) signals. Muscle forces are obtained by numerically integrating a system of ordinary differential equations (ODEs) that simulates Hill-type muscle dynamics and that utilises EMG signals as input. In the current version, the GUI can estimate the forces of lower limb muscles executing isometric contractions. Muscles from other parts of the body can be tested as well, although no default values for model parameters are provided. To achieve accurate evaluations, EMG collection is performed simultaneously with torque measurement from a dynamometer. The computer application guides the user, step-by-step, to pre-process the raw EMG signals, create inputs for the muscle model, numerically integrate the ODEs and analyse the results.

Results: An example of the application's functions is presented using the quadriceps femoris muscle. Individual muscle force estimations for the four components as well the knee isometric torque are shown.

Conclusions: The proposed GUI can estimate individual muscle forces from EMG signals of skeletal muscles. The estimation accuracy depends on several factors, including signal collection and modelling hypothesis issues.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995512PMC
http://dx.doi.org/10.1186/1475-925X-13-37DOI Listing

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