Skip to main content

A Python Implementation of the Conventional Gait Model

Project description

pyCGM

Python Module for the Conventional Gait Model calculates Kinematics and Center of Mass

The goal of this project is to release an easy to understand conventional gait model that users can implement in their own projects via a single python file. While the project is multiple files, the kinematics code is contained in a single file pyCGM.py. With the update, another kinetics file is also used for the center of mass calculation.

Our aim is to provide researchers and students a tool that can aid in understanding and developing modifications to the conventional gait model through python without much more.

The kinematics are validated against Nexus 1.8, and file types are known from Nexus 1.8. Newer C3D files may not work (but files re-exported from Mokka usually work).

How to use?

For getting started, please check the WIKI on github

Requirements:

  • Python 2.7 or Python 3
  • Numpy
  • Scipy (only if using the pipeline operations)

Requirements for HPC:

  • Python 2.7
  • Numpy
  • MPI Preferably Linux (MPI is not as simple to setup on windows)

Uses a modified version of the c3d.py loader from github. https://pypi.python.org/pypi/c3d/0.2.1

Credits:

Originally developed in the Digital Human Research Center at the Advanced Institutes of Convergence Technology (AICT), Seoul National University http://aict.snu.ac.kr

Project Lead: Mathew Schwartz (umcadop at gmail) For issues, use github or email me directly

Contributors: Neil M. Thomas, Philippe C. Dixon, Seungeun Yeon (연승은),Filipe Alves Caixeta, Robert Van-Wesep

Reference

Read about this code and if you find it useful in your work please cite:

Schwartz, Mathew, and Philippe C. Dixon. "The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods." PloS one 13.1 (2018): e0189984. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189984

Bibtex:

@article{schwartz2018effect, title={The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods}, author={Schwartz, Mathew and Dixon, Philippe C}, journal={PloS one}, volume={13}, number={1}, pages={e0189984}, year={2018}, publisher={Public Library of Science} }

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyCGM-1.0.tar.gz (80.7 kB view details)

Uploaded Source

Built Distribution

pyCGM-1.0-py3-none-any.whl (86.9 kB view details)

Uploaded Python 3

File details

Details for the file pyCGM-1.0.tar.gz.

File metadata

  • Download URL: pyCGM-1.0.tar.gz
  • Upload date:
  • Size: 80.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.50.1 CPython/3.8.3

File hashes

Hashes for pyCGM-1.0.tar.gz
Algorithm Hash digest
SHA256 dc3ba377f523c84ab51efad672fecfd5081e2916dd30741077411857774c8cd7
MD5 ccaebe948704f490e8ef185949083aa1
BLAKE2b-256 69fff9478f5d2df8703b63fdc72eb52edbae5f6e58528ea1b1fd314fc93a6bcc

See more details on using hashes here.

File details

Details for the file pyCGM-1.0-py3-none-any.whl.

File metadata

  • Download URL: pyCGM-1.0-py3-none-any.whl
  • Upload date:
  • Size: 86.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.50.1 CPython/3.8.3

File hashes

Hashes for pyCGM-1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 0ba7fe88e3858182da671e145c69ebc7f69142ad2ce3b6b93e96c1dd183667d3
MD5 224b8a39d51fd783d314daef68cae639
BLAKE2b-256 4960cf658db637d3dd73a675e7492e10f6302347a8549d3c029e948717d71906

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page