3D muscle networks based on vibrational mechanomyography

Area of Science:

• Biomedical Engineering
• Neuroscience
• Biomechanics

Background:

• Muscle network modeling is crucial for understanding synergistic control in motor tasks.
• Intermuscular coherence (IMC) helps identify synchronization in neuromuscular control.
• Electromyography (EMG) limitations in spatial and fiber information necessitate advanced techniques.

Purpose of the Study:

• To introduce and evaluate three-dimensional (3D) muscle networks using vibrational mechanomyography (vMMG) and IMC analysis.
• To assess functional muscle co-modulation across frequency bands and fiber directions.
• To determine the utility of vMMG-based networks for hand gesture classification and robustness to sensor-skin contact force.

Main Methods:

• Collected vMMG data from 20 subjects performing hand gestures using an accelerometer armband.
• Decomposed IMC from four superficial muscles into three frequency bands using matrix factorization.
• Analyzed vMMG network responses to varying sensor-skin contact forces.

Main Results:

• Identified distinct topological differences and significant coherent coupling (up to 57%) between muscle pairs, varying by frequency band, gesture, and direction.
• Found no statistical decrease in signal strength with increased contact force.
• Demonstrated vMMG-based network models are robust to variations in tissue compression.

Conclusions:

• vMMG is a viable tool for muscle connectivity analyses.
• IMC provides a novel feature space for hand gesture classification.
• 3D vMMG muscle network models offer a new foundation for studying synergistic muscle activation, especially in non-clinical settings.