Abstract
BackgroundThis 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.ResultsAn 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.ConclusionsThe 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.
Highlights
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
This paper presents a graphical user interface (GUI) that estimates muscle forces using a dynamic Hill-type EMG-driven model (EMG Driven Force Estimator (EMGD-FE) v1.0)
The forces are found by integrating a set of Ordinary differential equation (ODE) describing a Hill-type muscle model
Summary
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. 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. Estimating skeletal muscle forces in vivo is a challenging problem in biomechanics. A specific characteristic of the EMG-driven model used in EMGD-FE relies on that it is formulated entirely in terms of ordinary differential equations (ODEs) to represent muscle dynamics [6]. Numerical integration of the ODEs yields the dynamic state of the muscle model. It comprises the muscle active state (activation), the tendon force and the length of the contractile part of the musculotendon unit
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