Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study

Area of Science:

• Biomedical Engineering
• Neuroscience
• Biomechanics

Background:

• The relationship between surface electromyogram (sEMG) signals and muscle force is not fully understood, hindering neuromuscular system assessment.
• Accurate modeling is crucial for both healthy individuals and those with pathological conditions.

Purpose of the Study:

• To investigate the factors influencing the sEMG/force relationship.
• To develop and validate a model for predicting muscle force from sEMG signals.

Main Methods:

• Simulated High-Density sEMG (HD-sEMG) using a cylindrical model and muscle force using a twitch-based model for the Biceps Brachii muscle.
• Performed sensitivity analysis on neural, functional, and physiological parameters under isometric, non-fatiguing conditions.
• Quantified sEMG amplitude using Root Mean Squared (RMS) values and employed image segmentation for data fusion.

Main Results:

• The relationship between RMS (mV) and muscle force (N) can be accurately represented by a 3rd-degree polynomial equation.
• The coefficients of this polynomial model were found to be patient-specific.
• Model performance was evaluated against recent propositions using Normalized Root Mean Squared Error (NRMSE).

Conclusions:

• A 3rd-degree polynomial model effectively describes the sEMG/force relationship.
• Patient-specific physiological, anatomical, and neural parameters significantly influence this relationship.
• The findings provide a foundation for improved neuromuscular assessment tools.