# EMG-Based Feature Extraction and Classification for Prosthetic Hand Control

arXiv: https://arxiv.org/abs/2107.00733 · HTML: https://mlsanity.com/paper/2107.00733/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 1 Jul 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)
- [Convolutional Neural Networks for Speech Controlled Prosthetic Hands](https://mlsanity.com/paper/1910.01918/) (2019)
- [EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine](https://mlsanity.com/paper/2106.10561/) (2021)

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

- [Real-Time EMG Signal Classification via Recurrent Neural Networks](https://mlsanity.com/paper/2109.05674/) (2021)
- [EMG-Based Hand Gesture Recognition through Diverse Domain Feature Enhancement and Machine Learning-Based Approach](https://mlsanity.com/paper/2408.13723/) (2024)

## Abstract (arXiv)

In recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a shorter signal length. We first introduce a set of new feature extraction functions applying on each level of wavelet decomposition. Then, we propose a postprocessing approach to process the neural network outputs. The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to 95.5% for 800 msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods.