<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the shortcomings of previously developed models. This model started with the simulation of the single fibre action potential (SFAP), then the model of the single motor unit action potential (MUAP), afterwards the imitation of the train of MUAP and finally the modellig of the resultant sEMG signal which is the sum of the MUAPs trains. SFAP simulation was based on: i) the description of the volume conductor model which is composed of four layers (bone, muscle, fat and skin), ii) the description of the electrodes shapes and sizes as well as spatial filters, iii) and the transmebrane current. The proposed model shows its effectiveness in the possibility of carrying out practical work by simulation on the modelling of SFAP, MUAP, MUAPT and the sEMG signal. The most important result of this model is that signal processing tools can be applied to analyze and interpret real-world phenomena such as the effects of physiological, non physiological and sensing system parameters on the shape of the simulated sEMG signal.</p>
</abstract>

For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the shortcomings of previously developed models. This model started with the simulation of the single fibre action potential (SFAP), then the model of the single motor unit action potential (MUAP), afterwards the imitation of the train of MUAP and finally the modellig of the resultant sEMG signal which is the sum of the MUAPs trains. SFAP simulation was based on: i) the description of the volume conductor model which is composed of four layers (bone, muscle, fat and skin), ii) the description of the electrodes shapes and sizes as well as spatial filters, iii) and the transmebrane current. The proposed model shows its effectiveness in the possibility of carrying out practical work by simulation on the modelling of SFAP, MUAP, MUAPT and the sEMG signal. The most important result of this model is that signal processing tools can be applied to analyze and interpret real-world phenomena such as the effects of physiological, non physiological and sensing system parameters on the shape of the simulated sEMG signal.

Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model

Faculty of Technology, Department Electrical Systems Engineering

University of Boumerdes, Faculty of Technology

University of Sétif 1, Faculty of Technology

Department of Electronics, LIS Laboratory

The Scientific Bulletin of Electrical Engineering Faculty

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model"}

For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the...

Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model

Online veröffentlicht: 28. Feb. 2024

Seitenbereich: 82 - 92

DOI: https://doi.org/10.2478/sbeef-2023-0024

Schlüsselwörter
detection system, fibres, modelling, motor unit, surface electromyography

© 2023 Noureddine Messaoudi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Simulated Surface Electromyographic (SEMG) Signal Generation and Detection Model

Online veröffentlicht: 28. Feb. 2024

Seitenbereich: 82 - 92

DOI: https://doi.org/10.2478/sbeef-2023-0024

Schlüsselwörterdetection system, fibres, modelling, motor unit, surface electromyography

© 2023 Noureddine Messaoudi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
detection system, fibres, modelling, motor unit, surface electromyography