# Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

## TL;DR

This paper introduces a deep learning domain adaptation approach using recurrent neural networks to improve sEMG-based gesture recognition accuracy across different sessions and subjects, addressing variability issues.

## Contribution

It presents a novel domain adaptation model specifically designed for sEMG gesture recognition, outperforming existing methods on public datasets.

## Key findings

- Outperforms state-of-the-art methods on public datasets
- Effectively reduces inter-session and inter-subject variability
- Enhances recognition accuracy in sEMG-based gesture recognition

## Abstract

Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.