Muscle-related differences in mechanomyography–force relationships are model-dependent.

Area of Science:

• Biomechanics
• Human Physiology
• Muscle Physiology

Background:

• Mechanomyography (MMG) measures muscle activity via mechanical vibrations.
• Understanding the relationship between MMG amplitude (MMG(RMS)) and force is crucial for assessing muscle function.
• Previous models have limitations in accurately describing these complex relationships.

Purpose of the Study:

• To compare the efficacy of log-transform and polynomial regression models in characterizing MMG(RMS)–force relationships.
• To investigate differences in muscle activation patterns between upper limb (FDI) and lower limb (VL, RF) muscles.
• To determine which regression model better differentiates these muscle-specific responses.

Main Methods:

• Isometric ramp contractions were performed by healthy men for leg extensors and index finger muscles.
• MMG sensors were placed on vastus lateralis (VL), rectus femoris (RF), and first dorsal interosseous (FDI) muscles.
• Log-transform and polynomial regression models were applied to the MMG(RMS)–force data.

Main Results:

• The log-transform model revealed significant differences in intercepts (a terms) and slopes (b terms) between FDI, VL, and RF muscles.
• Polynomial regression models did not consistently identify significant differences in these parameters across the studied muscles.
• These findings suggest distinct patterns of muscle response during force generation.

Conclusions:

• The log-transform model provides a superior method for quantifying differences in mechanomyographic responses between hand and leg muscles.
• Polynomial regression models were less effective in distinguishing the nuanced patterns of muscle activation observed.
• This study highlights the importance of model selection for accurate interpretation of muscle electrophysiological data.