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gaitsetpy

https://github.com/alohomora-labs/gaitsetpy

Science Score: 67.0%

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Repository

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  • Host: GitHub
  • Owner: Alohomora-Labs
  • License: gpl-3.0
  • Language: HTML
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Created about 1 year ago · Last pushed 8 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

GaitSetPy

DOI PyPI version Docs CodeRabbit Pull Request Reviews

GaitSetPy is a Python package for gait analysis and recognition. This package provides tools and algorithms to process and analyze gait data, enabling researchers and developers to build applications for gait recognition and clinical gait assessment.

Features

  • Gait data preprocessing
  • Feature extraction
  • Gait recognition algorithms
  • Visualization tools

Supported Datasets

IMU Sensor Based

Pressure Sensor Based

Installation

You can install GaitSetPy using pip:

bash git clone https://github.com/Alohomora-Labs/gaitSetPy.git python setup.py install

Optionally, also install requirements bash pip install -r requirements.txt

Usage

Here is a simple example to get you started with GaitSetPy:

Daphnet Dataset Example

```python import gaitsetpy as gsp

Load gait data

daphnet, names = gsp.loaddaphnetdata("")

Preprocess data

slidingwindows = gsp.createsliding_windows(daphnet, names) freq = 64

Extract features

features = gsp.extractgaitfeatures(sliding_windows[0]['windows'], freq, True, True, True)

Visualize gait features

gsp.plotsensorwithfeatures(slidingwindows[0]['windows'], features, sensorname="shank", numwindows=15) ```

HAR-UP Dataset Example

```python import gaitsetpy as gsp

Load HAR-UP data

datadir = "data/harup" harupdata, harupnames = gsp.loadharupdata(datadir)

Create sliding windows

windowsize = 100 # 1 second at 100Hz stepsize = 50 # 0.5 second overlap windows = gsp.createharupwindows(harupdata, harupnames, windowsize, stepsize)

Extract features

featuresdata = gsp.extractharup_features(windows)

For more advanced usage, see examples/harup_example.py

``` alt text

``` python

Train a Random Forest

rfmodel = gsp.RandomForestModel(nestimators=50, randomstate=42, maxdepth=10) rf_model.train(features)

Load a pretrained model

rfmodel.loadpretrainedweights("randomforestmodel40_10.pkl")

Evaluate Model

gsp.evaluatemodel(rfmodel.model, features) # Assuming 'rf_model' is your trained RandomForestModel instance ```

Documentation

For detailed documentation and API reference, please visit the official documentation.

Contributing

We welcome contributions! Please read our contributing guidelines to get started.

License

This project is licensed under the GNU GPL License. See the LICENSE file for more details.

Contact

For any questions or inquiries, please contact us at jayeeta.chakrabortyfcs@kiit.ac.in or aharshit123456@gmail.com.

Owner

  • Name: Alohomora Labs
  • Login: Alohomora-Labs
  • Kind: organization

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: gaitSetPy
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
doi: 10.5281/zenodo.15881527
authors:
  - given-names: Harshit
    family-names: Agarwal
    email: 23052801@kiit.ac.in
    affiliation: Kalinga Institute of Industrial Technology
    orcid: 'https://orcid.org/0009-0000-2173-1740'
  - given-names: Jayeeta
    family-names: Chakraborty
    email: jayeeta.chakrabortyfcs@kiit.ac.in
    orcid: 'https://orcid.org/0000-0003-3918-1649'
    affiliation: Kalinga Institute of Industrial Technology
repository-code: 'https://github.com/Alohomora-Labs/gaitSetPy'
keywords:
  - gait
  - daphnet
  - imu tool kit
  - signals analysis
  - gait analysis tool kit
license: GPL-3.0+

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Dependencies

requirements.txt pypi
  • joblib *
  • matplotlib *
  • numpy *
  • pandas *
  • requests *
  • scikit-learn *
  • scipy *
  • statsmodels *
setup.py pypi
  • numpy *
  • pandas *
  • scipy *