Few-shot prototype adaptation for generalizable electromyography gesture recognition.

Area of Science:

• Biomedical Engineering
• Machine Learning
• Human-Computer Interaction

Background:

• Electromyography (EMG) based gesture recognition is crucial for advanced human-computer interfaces.
• Current methods often require extensive user-specific calibration data, limiting practical application.
• Few-shot learning presents a promising avenue for reducing data requirements.

Purpose of the Study:

• To introduce EMG-Adapt, a novel framework for robust and data-efficient few-shot electromyography (EMG) gesture recognition.
• To improve the generalization capabilities of EMG-based systems across different users and sessions.
• To reduce the amount of labeled data needed for effective gesture recognition.

Main Methods:

• Developed a few-shot prototype adaptation framework integrating prototype learning and meta-learning.
• Implemented a cepstrum coefficient average feature extraction method for noise reduction.
• Utilized deep prototype learning with hybrid loss functions and a meta-learning strategy for efficient adaptation.

Main Results:

• EMG-Adapt significantly improved few-shot gesture recognition performance across five public EMG datasets.
• Achieved state-of-the-art results in cross-session and cross-user generalization.
• Demonstrated substantial reduction in required calibration data compared to conventional methods.
• Maintained computational efficiency.

Conclusions:

• EMG-Adapt offers a significant advancement for practical, user-friendly, and scalable EMG-based human-computer interfaces.
• The framework shows strong potential for applications in prosthetics, assistive technologies, and virtual reality.
• Future work will focus on self-supervised learning and online adaptation for enhanced real-world robustness.