Kinetics Toolkit

Kinetics Toolkit is an open-source, pure-python package of integrated classes and functions that aims to facilitate research in biomechanics using python.

Kinetics Toolkit is mainly addressed to researchers and students in biomechanics with a little background in programming, who may or may not already have a working workflow and who want to understand and control their data. This is why special attention is made to build rich API documentation and tutorials, and to ensure the interoperability of ktk with other environments (using pandas Dataframes and JSON files as intermediate data containers).

Kinetics Toolkit is developed at the Mobility and Adaptive Sports Research Lab in Montreal.

Example

>>>    markers = ktk.kinematics.read_c3d_file('my_file.c3d')
>>>    ktk.Player(markers)
https://felixchenier.uqam.ca/wp-content/uploads/2020/05/Sample_ktk.Player_Wheelchair.gif

Stable version

The stable version API is mostly settled and well tested using a comprehensive set of unit tests and doc tests, and currently includes:

Lower level modules, classes and functions

  • TimeSeries : a generic class to represent time-varying n-dimensional data and events, with many methods to extract, merge and subset TimeSeries data.

  • filters : a module that wraps some filters from scipy to be applied directly on TimeSeries.

  • cycles : a module that detects, time-normalizes and stacks cycles (e.g., gait cycles, wheelchair propulsion cycles, etc.)

  • save and load : two functions to save and load results to/from JSON-based ktk.zip files.

  • other helper functions.

Higher level modules and classes

  • kinematics : a module that loads c3d and n3d files as TimeSeries of 3d marker positions.

  • player.Player : a class that allows visualizing 3d markers using a simple graphical user interface.

  • pushrimkinetics : a module that reads files from instrumented wheelchair wheels, reconstructs the pushrim kinetics, removes dynamic offsets in kinetic signals, and perform speed and power calculation for analysing spatiotemporal and kinetic parameters of wheelchair propulsion.

You can ask your questions and submit bugs or feature requests on ktk’s github issue tracker. However, please keep in mind that I develop Kinetics Toolkit primarily for my lab and I have limited resources for troubleshooting. But if I can answer, I’ll do.

Development version

The development version is developed in parallel with my research projects following the needs of the moment, and is therefore well less settled, tested and stable.

Credits

Kinetics Toolkit is developed by Professor Félix Chénier at Université du Québec à Montréal, Canada.

Some external code has been directly included into ktk’s source code. Here are the credits for these nice people.

  • Clay Flannigan : icp - Python implementation of m-dimensional Iterative Closest Point method

I also want to credit the people involved in ktk’s dependencies:

  • Benjamin Michaud : ezc3d - Easy to use C3D reader/writer for C++, Python and Matlab

  • The dedicated people behind major software and packages used by ktk such as python, numpy, matplotlib, pandas, jupyter, pytest, sphinx, etc.

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