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.
You must have NumPy, Pandas, Matplotlib, Scipy, and Pyyawt installed.
- digital_processing.py - It contains the digital filters (notch and band pass) configuration to eliminate signal noise and artifacts.
- feature_extraction.py - It allows to compute time and frequency features above an EMG signal.
- feature_extraction_scheme.py - It contains the feature extraction scheme: load data, biomedical signal processing and feature extraction.
- data - This folder contains the EMG data to be analyzed.
- Clone emg pattern recognition project in a local directory.
git clone https://github.com/SebastianRestrepoA/EMG-pattern-recognition.git
- 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
- Run feature_extraction_scheme.py using below command to see EMG characterization.
python feature_extraction_scheme.py