An Anatomically Based Surface EMG Model

M. M. Lowery*, N. S. Stoykov, T. A. Kuiken

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


A model of the surface EMG signal based on a realistic arm anatomy is presented. A finite element volume conductor model was developed using magnetic resonance (MR) images of the upper arm of a healthy male subject. The model includes both resistive and capacitive material properties. To examine the ability of the model to predict the potential distribution around the surface of the arm, experimental and simulated data were compared during the application of a sub-threshold current source to the skin surface. The agreement between the simulated and experimental data varied with the choice of material properties used, with the closest approximation to the experimental data yielding a mean root mean square (RMS) error at the recording electrodes of 18 % or 27 %, depending on the site of the applied current source. To examine the influence of limb geometry on the EMG signal, action potentials from a curved muscle fiber in the realistic volume conductor model and an idealized cylindrical model were compared. The specific geometry of the limb caused substantial variations in the shapes of the surface potentials. However, more qualitative features of the surface EMG signal, such as the rate of decay of the surface action potential amplitude, were similar in both the realistic and idealized volume conductor models.

Original languageEnglish (US)
Pages (from-to)2822-2825
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003


  • Finite element model
  • MR image
  • Surface EMG
  • Volume conductor

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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