# Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets

arXiv: https://arxiv.org/abs/2401.02773 · HTML: https://mlsanity.com/paper/2401.02773/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications](https://mlsanity.com/paper/2409.08058/) (2024)
- [MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition](https://mlsanity.com/paper/2502.17457/) (2025)
- [Masked Autoencoder with Swin Transformer Network for Mitigating Electrode Shift in HD-EMG-based Gesture Recognition](https://mlsanity.com/paper/2410.17261/) (2024)

## Abstract (arXiv)

sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions. Multi-channel surface EMG, also referred to as high-density sEMG (HD-sEMG) systems, have been used to improve performance with the information collected through the use of additional electrodes. However, a lack of robustness is ever present due to limited datasets and the difficulties in addressing sources of variability, such as electrode placement. In this study, we propose training on a collection of input channel subsets and augmenting our training distribution with data from different electrode locations, simultaneously targeting electrode shift and reducing input dimensionality. Our method increases robustness against electrode shift and results in significantly higher intersession performance across subjects and classification algorithms.