Simulations of high-density surface electromyograms in dynamic muscle contractions

Area of Science:

• Biomechanics
• Neuroscience
• Biomedical Engineering

Background:

• High-density surface electromyography (hdEMG) is crucial for analyzing skeletal muscle activity.
• Accurate simulation of dynamic muscle contractions is needed to understand hdEMG signals.
• Existing models often lack the ability to simulate dynamic changes in muscle architecture.

Purpose of the Study:

• To extend a cylindrical volume conductor model for simulating synthetic hdEMG during dynamic skeletal muscle contractions.
• To develop a modular simulator capable of modeling muscle shortening, thickening, and motor unit excitation profiles.
• To assess the impact of muscle shortening and thickening on motor unit identification using the Convolution Kernel Compensation (CKC) technique.

Main Methods:

• Simulated dynamic changes in motor unit action potentials (MUAPs) across 36 steps of muscle shortening.
• Modeled the increase in motor unit (MU) depth due to muscle fiber shortening and thickening.
• Generated MU firing patterns using the Fuglevand et al. model and convolved them with simulated MUAPs.
• Utilized the Convolution Kernel Compensation (CKC) technique to analyze the impact of simulated muscle dynamics on MU identification.

Main Results:

• Developed a novel hdEMG simulator for dynamic muscle contractions, incorporating muscle shortening and thickening.
• Demonstrated the simulator's capability to generate realistic dynamic hdEMG signals for various muscle conditions.
• Quantified the independent effects of muscle shortening and thickening on the accuracy of MU identification via CKC.

Conclusions:

• The developed hdEMG simulator provides a valuable tool for investigating muscle contraction dynamics.
• Understanding the influence of muscle architecture changes is essential for accurate MU analysis from hdEMG.
• This simulation framework can advance research in neuromuscular physiology and clinical diagnostics.