Myosim 2.0: Enhancing and Validating an EMG Simulation Tool

  • D. MacIsaac University of New Brunswick, Fredericton
  • S Tallam Puranam Raghu University of New Brunswick, Fredericton
  • Y Shi University of New Brunswick, Fredericton

Abstract

In 2006, we presented a MATLAB tool called Myosim, which allows users simulate signals measured through surface electromyography (SEMG). Based on user input, the tool sets generative model parameter values such as the geometry of each fibre in each motor unit relative to the electrode location, the number of motor units, the number of fibres per motor unit, conduction velocity and firing statistics. Using the parameter values, the tool then outputs an SEMG signal based on a finite length model of muscle and a convolution between action potential source and tissue filter. Recently we have made updates to the tool which improve the underlying model used to generate the signal, and allow users to add instrumentation effects associated with the data capture process including baseline noise, band-pass filtering, and quantization. We have also used a genetic algorithm to select generative model parameter values which optimize matching between real and simulated signals, to validate that the tool produces output representative of EMG.
Published
2018-05-08
How to Cite
[1]
D. MacIsaac, S. Tallam Puranam Raghu, and Y. Shi, “Myosim 2.0: Enhancing and Validating an EMG Simulation Tool”, CMBES, vol. 41, May 2018.
Section
Academic