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

arXiv: https://arxiv.org/abs/1901.06958 · HTML: https://mlsanity.com/paper/1901.06958/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 15 of 15)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning](https://mlsanity.com/paper/2109.14778/) (2021)
- [ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals](https://mlsanity.com/paper/2201.10060/) (2022)
- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)
- [Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning](https://mlsanity.com/paper/2305.08014/) (2023)
- [Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification](https://mlsanity.com/paper/2112.15019/) (2021)
- [Recognition and Label-Free Adaptation Across Recording Sessions in Surface-EMG Gesture Decoding](https://mlsanity.com/paper/2607.27568/) (2026)
- [Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition](https://mlsanity.com/paper/2306.15114/) (2023)
- [Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications](https://mlsanity.com/paper/2409.08058/) (2024)
- [Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics](https://mlsanity.com/paper/2504.03221/) (2025)
- [GesSure- A Robust Face-Authentication enabled Dynamic Gesture Recognition GUI Application](https://mlsanity.com/paper/2207.11033/) (2022)
- [Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains](https://mlsanity.com/paper/2212.00743/) (2022)
- [Masked Autoencoder with Swin Transformer Network for Mitigating Electrode Shift in HD-EMG-based Gesture Recognition](https://mlsanity.com/paper/2410.17261/) (2024)
- [Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition](https://mlsanity.com/paper/2412.15819/) (2024)
- [Personalising a Cross-User Surface Electromyography Encoder Under a Small Calibration Budget](https://mlsanity.com/paper/2609.20296/) (2026)

## Abstract (arXiv)

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.