# EMG subspace alignment and visualization for cross-subject hand gesture classification

arXiv: https://arxiv.org/abs/2401.05386 · HTML: https://mlsanity.com/paper/2401.05386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Subspace Alignment For Domain Adaptation](https://mlsanity.com/paper/1409.5241/) (2014)

## Abstract (arXiv)

Electromyograms (EMG)-based hand gesture recognition systems are a promising technology for human/machine interfaces. However, one of their main limitations is the long calibration time that is typically required to handle new users. The paper discusses and analyses the challenge of cross-subject generalization thanks to an original dataset containing the EMG signals of 14 human subjects during hand gestures. The experimental results show that, though an accurate generalization based on pooling multiple subjects is hardly achievable, it is possible to improve the cross-subject estimation by identifying a robust low-dimensional subspace for multiple subjects and aligning it to a target subject. A visualization of the subspace enables us to provide insights for the improvement of cross-subject generalization with EMG signals.