dspanah/Sensor-Based-Human-Activity-Recognition-LSTMsEnsemble-Pytorch

Ensembles of Deep LSTM Learners for Human Activity Recognition using Wearables in Pytorch

Overview

This repository includes the Pytorch implementation of the paper "Ensembles of Deep LSTM Learners for Activity Recognition using Wearables" by Yu Guan and Thomas Plötz, which is available at: https://doi.org/10.1145/3090076

You can find the authors' original implementation in tensorflow at: https://github.com/tploetz/LSTMEnsemble4HAR

To run the code, open up "1.0-dsp-LSTMsEnsemble.ipynb" jupyter notebook under notebooks folder and follow the step by step instructions.

Dependencies

• Python 3
  
• Pytorch

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data     
│   └── processed      <- The final, canonical data sets for modeling.
│
├── models             <- Trained models
│
├── notebooks          <- Jupyter notebooks. 
│    └── 1.0-dsp-LSTMsEnsemble.ipynb  <-- Full Pipeline in a step by step manner                   
│                       
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    └── data           <- Scripts to download or generate data
        └── dataset.py      

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Project based on the cookiecutter data science project template. #cookiecutterdatascience