https://github.com/alcantarar/recurrent_grf_prediction

Repository supporting "Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution"

https://github.com/alcantarar/recurrent_grf_prediction

Science Score: 10.0%

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Keywords

biomechanics rnn wearable-devices
Last synced: 10 months ago · JSON representation

Repository

Repository supporting "Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution"

Basic Info
  • Host: GitHub
  • Owner: alcantarar
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 28.5 MB
Statistics
  • Stars: 9
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Topics
biomechanics rnn wearable-devices
Created over 5 years ago · Last pushed over 4 years ago
Metadata Files
Readme License

README.md

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

visitors

This repository supports 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.

Contents

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 LSTM_Example.ipynb and Test_LSTM.ipynb.

data/ Contains example accelerometer data, GRF data, condition/demographic data, and LSTM model file. Supports Test_LSTM.ipynb and LSTM_Example.ipynb.

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 Google Colab.

Questions?

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.

Owner

  • Name: Ryan Alcantara
  • Login: alcantarar
  • Kind: user
  • Location: Stanford, CA
  • Company: NMBL

Postdoc at Stanford Neuromuscular Biomechanics Lab (NMBL)

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