This is a collection of tools that are helpful for gait analysis. Some are specific to the needs of the Human Motion and Control Lab at Cleveland State University but other portions may have potential for general use. It is relatively modular so you can use what you want. It is primarily structured as a Python distribution but the Octave files are also accessible independently.

Latest Version

Python Packages

The main Python package is gaitanalysis and it contains five modules listed below. oct2py is used to call Octave routines in the Python code where needed.
General tools for working with gait data such as gait landmark identification and 2D inverse dynamics. The main class is GaitData.
Tools for identifying control mechanisms in human locomotion.
Routines for processing marker data.
Tools for processing and cleaning data from Motek Medical‘s products, e.g. the D-Flow software outputs.
Helper functions for the other modules.

Each module has a corresponding test module in gaitanalysis/tests sub-package which contain unit tests for the classes and functions in the respective module.

Octave Libraries

Several Octave routines are included in the gaitanalysis/octave directory.

Implements joint angle and moment computations of a 2D lower body human.
Compensates force plate forces and moments for inertial effects and re-expresses the forces and moments in the camera reference frame.
Fast matrix multiplication.
Computes the rigid body orientation and location of a group of markers.
Deals with the analog signal time delays.


You will need Python 2.7 and setuptools to install the packages. Its best to install the dependencies first (NumPy, SciPy, matplotlib, Pandas, PyTables). The SciPy Stack instructions are helpful for this:

Supported versions:

  • python >= 2.7
  • numpy >= 1.6.1
  • scipy >= 0.9.0
  • matplotlib >= 1.1.0
  • tables >= 2.3.1
  • pandas >= 0.12.0
  • pyyaml >= 3.10
  • DynamicistToolKit >= 0.3.5
  • oct2py >= 1.2.0
  • octave >= 3.8.1

We recommend installing Anaconda for users in our lab to get all of the dependencies.

We also utilize Octave code, so an install of Octave with is also required. See for installation instructions.

You can install using pip (or easy_install). Pip will theoretically [1] get the dependencies for you (or at least check if you have them):

$ pip install

Or download the source with your preferred method and install manually.

Using Git:

$ git clone
$ cd GaitAnalysisToolKit

Or wget:

$ wget
$ unzip
$ cd GaitAnalysisToolKit-master

Then for basic installation:

$ python install

Or install for development purposes:

$ python develop
[1]You will need all build dependencies and also note that matplotlib doesn’t play nice with pip.


It is recommended to install the software dependencies as follows:

Octave can be installed from your package manager or from a downloadable binary, for example on Debian based Linux:

$ sudo apt-get install octave

For oct2py to work, calling Octave from the command line should work after Octave is installed. For example,

$ octave
GNU Octave, version 3.8.1
Copyright (C) 2014 John W. Eaton and others.
This is free software; see the source code for copying conditions.
FITNESS FOR A PARTICULAR PURPOSE.  For details, type 'warranty'.

Octave was configured for "x86_64-pc-linux-gnu".

Additional information about Octave is available at

Please contribute if you find this software useful.
For more information, visit

Read to learn how to submit bug reports.
For information about changes from previous versions, type 'news'.


The core dependencies can be installed with conda in a conda environment:

$ conda create -n gait python=2.7 pip numpy scipy matplotlib pytables pandas pyyaml nose sphinx
$ source activate gait

And the dependencies which do not have conda packages can be installed into the environment with pip:

(gait)$ pip install DynamicistToolKit oct2py


When in the repository directory, run the tests with nose:

$ nosetests


A vagrant file and provisioning script are included to test the code on both a Ubuntu 12.04 and Ubuntu 13.10 box. To load the box and run the tests simply type:

$ cd vagrant
$ vagrant up

See VagrantFile and the * files to see what’s going on.


The documentation is hosted at ReadTheDocs:

You can build the documentation (currently sparse) if you have Sphinx and numpydoc:

$ cd docs
$ make html
$ firefox _build/html/index.html


The recommended procedure for contributing code to this repository is detailed here. It is the standard method of contributing to Github based repositories (

If you have don’t have access rights to this repository then you should fork the repository on Github using the Github UI and clone the fork that you just made to your machine:

git clone<your-username>/GaitAnalysisToolKit.git

Change into the directory:

cd GaitAnalysisToolKit

Now, setup a remote called upstream that points to the main repository so that you can keep your local repository up-to-date:

git remote add upstream

Now you have a remote called origin (the default) which points to your Github account’s copy and a remote called upstream that points to the main repository on the csu-hmc organization Github account.

It’s best to keep your local master branch up-to-date with the upstream master branch and then branch locally to create new features. To update your local master branch simply:

git checkout master
git pull upstream master

If you have access rights to the main repository simply, clone it and don’t worry about making a fork on your Github account:

git clone

Change into the directory:

cd GaitAnalysisToolKit

Now, to contribute a change to the repository you should create a new branch off of the local master branch:

git checkout -b my-branch

Now make changes to the software and be sure to always include tests! Make sure all tests pass on your machine with:


Once tests pass, add any new files you created:

git add

Now commit your changes:

git commit -am "Added an amazing new feature."

Push your commits to a mirrored branch on the Github repository that you cloned:

git push origin my-branch

Now visit the repository on Github (either yours or the main one) and you should see a “compare and pull button” to make a pull request against the main repository. Github and Travis-CI will check for merge conflicts and run the tests again on a cloud machine. You can ask others to review your code at this point and if all is well, press the “merge” button on the pull request. Finally, delete the branches on your local machine and on your Github repo:

git branch -d my-branch && git push origin :my-branch

Git Notes

  • The master branch on main repository on Github should always pass all tests and we should strive to keep it in a stable state. It is best to not merge contributions into master unless tests are passing, and preferably if someone else approved your code.
  • In general, do not commit changes to your local master branch, always pull in the latest changes from the master branch with git pull upstream master then checkout a new branch for your changes. This way you keep your local master branch up-to-date with the main master branch on Github.
  • In general, do not push changes to the main repo master branch directly, use branches and push the branches up with a pull request.
  • In general, do not commit binary files, files generated from source, or large data files to the repository. See for some reasons.

Release Notes


  • Fixed bug preventing GaitData.plot_grf_landmarks from working.
  • Removed inverse_data.mat from the source distribution.


  • Fixed installation issue where the octave and data files were not included in the installation directory.


  • Initial release
  • Copied the walk module from DynamicistToolKit @ eecaebd31940179fe25e99a68c91b75d8b8f191f

Indices and tables