FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography

TL;DR

This paper introduces FS-HGR, a few-shot learning framework based on meta-learning, enabling accurate hand gesture recognition from surface electromyogram signals with minimal training data, suitable for real-world prosthetic applications.

Contribution

The paper presents a novel few-shot learning approach for hand gesture recognition using sEMG signals, addressing data scarcity issues in practical scenarios.

Findings

Achieved 85.94% accuracy on new repetitions with few-shot learning.

Achieved 81.29% accuracy on new subjects with few-shot learning.

Achieved 73.36% accuracy on new gestures with few-shot learning.

Abstract

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through processing of surface electromyogram (sEMG) signals. The ultimate goal of these approaches is to realize high-performance controllers for prosthetic. However, although DNNs have shown superior accuracy than conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. Therefore, there is an unmet need for the design of a modern gesture detection technique that relies on minimal training data while providing high accuracy. Here we propose an innovative and novel "Few-Shot Learning" framework based on the formulation of meta-learning, referred to as the FS-HGR, to address this need. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training examples. More specifically, the proposed FS-HGR quickly generalizes after seeing very few examples from each class. The proposed approach led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot).