# Real-Time EMG Signal Classification via Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2109.05674 · HTML: https://mlsanity.com/paper/2109.05674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 13 Sep 2021.

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

- [Deep learning approach to control of prosthetic hands with electromyography signals](https://mlsanity.com/paper/1909.09910/) (2019)
- [EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine](https://mlsanity.com/paper/2106.10561/) (2021)
- [EMG-Based Feature Extraction and Classification for Prosthetic Hand Control](https://mlsanity.com/paper/2107.00733/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)
- [Hardware-Efficient EMG Decoding for Next-Generation Hand Prostheses](https://mlsanity.com/paper/2405.20052/) (2024)

## Abstract (arXiv)

Real-time classification of Electromyography signals is the most challenging part of controlling a prosthetic hand. Achieving a high classification accuracy of EMG signals in a short delay time is still challenging. Recurrent neural networks (RNNs) are artificial neural network architectures that are appropriate for sequential data such as EMG. In this paper, after extracting features from a hybrid time-frequency domain (discrete Wavelet transform), we utilize a set of recurrent neural network-based architectures to increase the classification accuracy and reduce the prediction delay time. The performances of these architectures are compared and in general outperform other state-of-the-art methods by achieving 96% classification accuracy in 600 msec.