Towards Next-Generation Myoelectric Prostheses: 3D-Printed Electrode Arrays for Gesture Recognition

Area of Science:

• Biomedical Engineering
• Materials Science
• Wearable Technology

Background:

• Electromyography (EMG) systems require effective electrode interfaces for accurate muscle signal detection.
• Existing EMG electrodes can be costly and lack customization for diverse anatomical applications.
• 3D printing offers potential for creating customized, low-cost, and complex electrode geometries.

Purpose of the Study:

• To design, fabricate, and evaluate a novel 12-channel 3D-printed electrode array for EMG.
• To assess the array's performance in recognizing hand gestures using wearable myoelectric technology.
• To demonstrate a cost-effective and customizable solution for EMG applications.

Main Methods:

• A dual-material 3D printing approach was used, combining flexible thermoplastic polyurethane (TPU) with conductive Protopasta® Composite PLA.
• A 12-channel electrode array was fabricated with a design optimized for conformal contact on the forearm.
• Electromyography signals were recorded from 10 participants performing six hand gestures, and data was classified using linear discriminant analysis with wavelet energy.

Main Results:

• The 3D-printed electrode array successfully recorded EMG signals during hand gesture tasks.
• Average hand gesture classification accuracy of (91.32 ± 7.23)% was achieved.
• The fabricated array demonstrated reliable motion recognition capabilities.

Conclusions:

• The 12-channel 3D-printed electrode array is a viable and effective tool for EMG-based hand gesture recognition.
• This technology presents a cost-effective, customizable, and high-performance alternative for wearable myoelectric systems.
• The study validates the potential of 3D printing in advancing EMG electrode design and functionality.