EMG Feature Extraction

This project explains how to apply digital filters above a raw EMG signal and then extract time and frequency features using the sliding window method. This characterization can be used as input to train a machine learning model that recognizes muscular patterns.

Prerequisites

You must have NumPy, Pandas, Matplotlib, Scipy, and Pyyawt installed.

Project Structure

1. digital_processing.py - It contains the digital filters (notch and band pass) configuration to eliminate signal noise and artifacts.
2. feature_extraction.py - It allows to compute time and frequency features above an EMG signal.
3. feature_extraction_scheme.py - It contains the feature extraction scheme: load data, biomedical signal processing and feature extraction.
4. data - This folder contains the EMG data to be analyzed.

Running the project

1. Clone emg pattern recognition project in a local directory.

git clone https://github.com/SebastianRestrepoA/EMG-pattern-recognition.git

2. Create enviroment for run EMG pattern recognition project. In your cloned folder run the following commands:

virtualenv env
env\Scripts\activate
pip install pandas
pip install matplotlib
pip install numpy
pip install pyyawt 

4. Run feature_extraction_scheme.py using below command to see EMG characterization.

python feature_extraction_scheme.py