Evaluation of HD-sEMG Probability Density Function deformations in ramp exercise

Area of Science:

• Biomedical Engineering
• Neuroscience
• Sports Science

Background:

• Surface electromyography (sEMG) is crucial for assessing muscle activity.
• High Density sEMG (HD-sEMG) offers detailed spatial information.
• Analyzing sEMG signal characteristics, like Probability Density Function (PDF) shape, can reveal underlying physiological changes.

Purpose of the Study:

• To propose a subject-specific screening approach for HD-sEMG PDF shape evolution.
• To investigate the trend of High Order Statistics (HOS) during isometric contractions.
• To analyze Biceps Brachii activity from 0% to 50% Maximum Voluntary Contraction (MVC).

Main Methods:

• Utilized an 8x8 HD-sEMG grid on the Biceps Brachii during ramp isometric contractions.
• Applied High Order Statistics (HOS), specifically kurtosis and skewness, to analyze PDF shape.
• Selected Laplacian sEMG channels based on Signal to Noise Ratio (SNR) for high muscle activation areas.
• Employed Principal Component Analysis (PCA) to identify trends in HOS with varying force levels.

Main Results:

• Identified a subject-specific screening approach for HD-sEMG.
• Demonstrated a globally common increasing trend in HOS (kurtosis and skewness) with increasing force (0-50% MVC).
• Observed consistent HOS trends across subjects despite individual variability.

Conclusions:

• The proposed HOS-based screening method effectively captures PDF shape evolution in HD-sEMG.
• A common, increasing HOS trend exists with increasing muscle force, offering a robust biomarker.
• This approach can standardize sEMG analysis and potentially reduce variability in experimental outcomes.