Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Science

Background:

• Functional muscle networks are crucial for complex motor tasks, typically analyzed using electromyography (EMG).
• Current methods using EMG for intermuscular coherence (IMC) have limitations for out-of-clinic applications.
• Understanding upper-limb muscle coordination is vital for daily activities and advanced human-robot interfaces.

Purpose of the Study:

• To introduce and validate mechanomyography (MMG) for functional muscle network analysis.
• To assess functional coordination and connectivity of upper-limb muscles during hand gestures.
• To investigate differences in muscle networks between able-bodied individuals and upper-limb amputees.

Main Methods:

• Developed a wearable MMG sensor armband to record muscle activity from forearm muscles (FCR, BR, EDC, FCU).
• Analyzed functional muscle networks using MMG data from ten able-bodied participants and three upper-limb amputees performing four hand gestures.
• Evaluated muscle connectivity across different frequency bands (e.g., 5 Hz, 12 Hz).

Main Results:

• MMG-based muscle connectivity analysis revealed significant topographical differences across various hand gestures.
• Observable differences in functional muscle networks were detected between amputee and non-amputee subjects.
• Results demonstrated MMG's capability to map muscle coherence and functional synchronization in complex movements.

Conclusions:

• Mechanomyography is a viable modality for analyzing functional muscle connectivity and synchronization in the upper limb.
• This approach provides insights into the neural circuitry underlying motor coordination.
• Findings support the translation of MMG for applications in human-robot interfaces and clinical rehabilitation.