Loofah Fiber-Reinforced Eutectogel for Motion-Robust and High-Fidelity Surface Electromyography

Area of Science:

• Biomedical Engineering
• Materials Science
• Wearable Technology

Background:

• Wearable surface electromyography (sEMG) systems struggle with signal degradation and motion artifacts during dynamic movements due to skin deformation.
• Existing systems lack robustness, limiting accurate movement recognition in real-world applications.

Purpose of the Study:

• To develop a wearable sEMG platform for reliable and high-fidelity signal acquisition during dynamic motion.
• To address challenges of signal degradation and motion artifacts in current sEMG technology.
• To enable accurate classification of lower-limb movements for various applications.

Main Methods:

• Integration of a loofah fiber-reinforced eutectogel with a perforated flexible printed circuit (FPC).
• Design features include stress-dispersing fibers for gel integrity and a perforated FPC for enhanced skin adhesion.
• A six-channel electrode array was utilized for monitoring synergistic muscle activation and durability testing over 100,000 bending cycles.

Main Results:

• The platform demonstrated robust signal acquisition under dynamic movements with up to 40% mechanical strain.
• Achieved excellent durability over 100,000 bending cycles.
• Real-time classification of lower-limb movements using machine learning resulted in 96.25% recognition accuracy.
• Demonstrated high biocompatibility with a 99.99% cell survival rate after 7-day cultivation.

Conclusions:

• The material-structural integrated strategy provides robust bioelectric sensing in high-dynamic scenarios.
• The developed platform offers promising applications in intelligent training, personalized rehabilitation, and human-computer interaction.
• This innovation enhances the reliability and accuracy of wearable sEMG for advanced biomechanical analysis.