How Many Muscles? Optimal Muscles Set Search for Optimizing Myocontrol Performance

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Technology

Background:

• Myoelectric control systems often face limitations due to the high number of muscles required for accurate control.
• Reducing the muscle set in myoelectric control is essential for practical applications, especially in rehabilitation, but has not been systematically investigated.
• Maintaining the integrity of muscle synergies, which represent physiological motor coordination, is critical when reducing the muscle set.

Purpose of the Study:

• To investigate the existence and characteristics of a minimum muscle set (optimal set) for upper-limb myoelectric applications.
• To determine if a reduced muscle set can maintain high performance in motor activity prediction and preserve the physiological meaning of muscle synergies.
• To explore subject-specific and global optimal muscle sets for myoelectric control.

Main Methods:

• Analysis of isometric contractions during planar reaching tasks.
• Identification and comparison of subject-specific and global optimal muscle sets.
• Extraction and analysis of muscle synergies (Pose-Shared and Pose-Related) from the global optimal set.
• Assessment of synergy similarity using Pearson correlation coefficients.

Main Results:

• A reduced muscle set can achieve comparable hand force estimation performance to a full muscle set.
• The global optimal muscle set, comprising recurrent muscles across subjects, demonstrated high consistency with full-set muscle synergies.
• Extracted muscle synergies from the optimal set showed significant similarity to those from the full set, indicating preserved motor primitives.
• Optimization of muscle set size and force estimation error may provide insights into the relationship between synergistic patterns and force tasks.

Conclusions:

• An optimal, reduced muscle set exists for upper-limb myoelectric control that balances performance and practical constraints.
• This optimal set preserves the physiological information encoded in muscle synergies, crucial for understanding motor coordination.
• The findings support the development of more efficient and sensitive myoelectric control systems for rehabilitation and other applications.