sEMG Gesture Recognition with a Simple Model of Attention

TL;DR

This paper introduces a simple yet effective attention-based model for sEMG gesture recognition that outperforms complex state-of-the-art methods across multiple datasets, highlighting its potential for diverse biomedical applications.

Contribution

The paper presents a novel, straightforward attention-based neural network model that achieves industry-leading results in sEMG gesture classification, surpassing more complex approaches.

Findings

Achieves top benchmark results on multiple sEMG datasets.

Outperforms CNN-based and traditional signal processing methods.

Demonstrates robustness with consumer-grade sensors.

Abstract

Myoelectric control is one of the leading areas of research in the field of robotic prosthetics. We present our research in surface electromyography (sEMG) signal classification, where our simple and novel attention-based approach now leads the industry, universally beating more complex, state-of-the-art models. Our novel attention-based model achieves benchmark leading results on multiple industry-standard datasets including 53 finger, wrist, and grasping motions, improving over both sophisticated signal processing and CNN-based approaches. Our strong results with a straightforward model also indicate that sEMG represents a promising avenue for future machine learning research, with applications not only in prosthetics, but also in other important areas, such as diagnosis and prognostication of neurodegenerative diseases, computationally mediated surgeries, and advanced robotic control. We reinforce this suggestion with extensive ablative studies, demonstrating that a neural network can easily extract higher order spatiotemporal features from noisy sEMG data collected by affordable, consumer-grade sensors.