Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
The final models and data supporting the published manuscript are archived here.
Train_LSTM.ipynb is a notebook that generates the model from the archived data.
Test_LSTM.ipynb is a notebook that shows you how to use the trained LSTM to predict GRFs from your own accelerometer data.
LSTM_Example.ipynb is a notebook that provides a tutorial of how a Long Short-Term Memory Network (LSTM) can be used to
predict ground reaction force (GRF) data from accelerometer data during running.
pre_processing.py contains helper functions used in
data/ Contains example accelerometer data, GRF data, condition/demographic data, and LSTM model file. Supports
If you're going to train an LSTM model using Google Colab (recommended), make sure
you utilize their GPU Runtime Type. You will need to adjust the path to
data/ depending on how files are uploaded in
Open an issue if you have a question or if something is broken. You can also email me at the address listed in the associated publication.