# Masked Autoencoder with Swin Transformer Network for Mitigating Electrode Shift in HD-EMG-based Gesture Recognition

arXiv: https://arxiv.org/abs/2410.17261 · HTML: https://mlsanity.com/paper/2410.17261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling](https://mlsanity.com/paper/2302.00861/) (2023)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks](https://mlsanity.com/paper/1901.06958/) (2019)
- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)
- [ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals](https://mlsanity.com/paper/2201.10060/) (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)
- [S-ConvNet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images](https://mlsanity.com/paper/1906.03381/) (2019)
- [ViT-MDHGR: Cross-day Reliability and Agility in Dynamic Hand Gesture Prediction via HD-sEMG Signal Decoding](https://mlsanity.com/paper/2309.12602/) (2023)
- [Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets](https://mlsanity.com/paper/2401.02773/) (2024)

## Abstract (arXiv)

Multi-channel surface Electromyography (sEMG), also referred to as high-density sEMG (HD-sEMG), plays a crucial role in improving gesture recognition performance for myoelectric control. Pattern recognition models developed based on HD-sEMG, however, are vulnerable to changing recording conditions (e.g., signal variability due to electrode shift). This has resulted in significant degradation in performance across subjects, and sessions. In this context, the paper proposes the Masked Autoencoder with Swin Transformer (MAST) framework, where training is performed on a masked subset of HDsEMG channels. A combination of four masking strategies, i.e., random block masking; temporal masking; sensor-wise random masking, and; multi-scale masking, is used to learn latent representations and increase robustness against electrode shift. The masked data is then passed through MAST's three-path encoder-decoder structure, leveraging a multi-path Swin-Unet architecture that simultaneously captures time-domain, frequency-domain, and magnitude-based features of the underlying HD-sEMG signal. These augmented inputs are then used in a self-supervised pre-training fashion to improve the model's generalization capabilities. Experimental results demonstrate the superior performance of the proposed MAST framework in comparison to its counterparts.