# Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds

arXiv: https://arxiv.org/abs/2311.08548 · HTML: https://mlsanity.com/paper/2311.08548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels](https://mlsanity.com/paper/1412.0265/) (2014)
- [TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition](https://mlsanity.com/paper/2109.12379/) (2021)

## Built on by (most cited first, 1 of 1)

- [Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography](https://mlsanity.com/paper/2411.02591/) (2024)

## Abstract (arXiv)

textitObjective. In this article, we present data and methods for decoding hand gestures using surface electromyogram (EMG) signals. EMG-based upper limb interfaces are valuable for amputee rehabilitation, artificial supernumerary limb augmentation, gestural control of computers, and virtual and augmented reality applications. textitApproach. To achieve this, we collect EMG signals from the upper limb using surface electrodes placed at key muscle sites involved in hand movements. Additionally, we design and evaluate efficient models for decoding EMG signals. textitMain results. Our findings reveal that the manifold of symmetric positive definite (SPD) matrices serves as an effective embedding space for EMG signals. Moreover, for the first time, we quantify the distribution shift of these signals across individuals. textitSignificance. Overall, our approach demonstrates significant potential for developing efficient and interpretable methods for decoding EMG signals. This is particularly important as we move toward the broader adoption of EMG-based wrist interfaces.