Motion capture running analyses are often performed on conventional (non-instrumented) treadmills. The absence of force plates can be problematic for the detection of foot strike and toe off, which are critical for the comprehensive biomechanical analysis of running kinematics. We introduce FootNet an algorithm for the detection of foot strike and toe off events on non-instrumented treadmills using segment and joint kinematics as input. The algorithm is based on an LSTM neural network architecture that has been trained, validated and tested using five datasets collected in three independent motion capture labs.
The publication associated with this study can be found here.
These instructions will get a copy of the project up and running on your local machine for development and testing purposes.
- Python 3.7
- venv >= 19.0 (if using pip)
- Clone this repository and enter it:
git clone https://github.com/adrianrivadulla/FootNet.git
cd FootNet- Create output directory where ouput data will be saved for the demo:
mkdir outputThis same process can be replicated manually by downloading and unzipping this repository, then creating the "output" folder in FootNet.
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Set up the environment using one of the following methods:
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Using Anaconda, navigate to the FootNet directory and create a version environment with the environment.yml file provided:
cd /path/to/FootNet conda env create -f environment.yml -
Using pip on Windows:
python -m venv --system-site-packages .\venv .\venv\Scripts\activate pip install --upgrade pip pip install -r requirements.txt
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Using pip on Ubuntu/macOS:
python3 -m venv --system-site-packages ./venv source ./venv/bin/activate # sh, bash, or zsh pip install --upgrade pip pip install -r requirements.txt
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Finally, verify the tensorflow install:
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random normal([1000, 1000])))"If a tensor is returned, you've installed TensorFlow successfully. If an error is returned see tensorflow website.
FootNet_v1 is deployed in FootNet_inference.py, navigate to the FootNet directory and use:
- Using default args (note that sampling frequency -sf must be added):
python FootNet_inference.py -sf 200- To view all args:
python FootNet_inference.py --helpSee docstring in FootNet_inference.py for detailed information on each input argument and output argument.
A further example is provided to demonstrate the results of FootNet against force plates. To run use:
python FootNet_compare_to_FP.pyAlthough Matlab has a new function called importKerasNetwork, as of January 2021, this function does not work as one would expect. A workaround to use FootNet through Matlab is included in the CMD-Matlab folder.
Copyright (c) 2021 Adrian R Rivadulla
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with this program (see gpl.txt and lgpl.txt). If not, see https://www.gnu.org/licenses/.
If you use FootNet or this code base in your work, please cite:
Rivadulla A, Chen X, Weir G, Cazzola D, Trewartha G, Hamill J, et al. (2021) Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running. PLoS ONE 16(8): e0248608. https://doi.org/10.1371/journal.pone.0248608
FootNet training can be replicated on Google Colab. More details on FootNet cross-validation and model testing, how to download the data and run the notebooks can be found in the notebooks folder.
For questions about our paper or code, please contact Adrian R.