https://github.com/trybnetic/tu7-acceleration-sleep-wake-classification
Supporting material for the paper ''Discrimination of sleep and wake periods from a hip-worn raw acceleration sensor using recurrent neural networks''
https://github.com/trybnetic/tu7-acceleration-sleep-wake-classification
accelerometer accelerometry actigraphy data-analysis sensors sleep
Last synced: 17 days ago
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Supporting material for the paper ''Discrimination of sleep and wake periods from a hip-worn raw acceleration sensor using recurrent neural networks''
- Host: GitHub
- URL: https://github.com/trybnetic/tu7-acceleration-sleep-wake-classification
- Owner: Trybnetic
- Created: 2021-04-23T09:30:13.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2022-02-08T09:09:31.000Z (over 4 years ago)
- Last Synced: 2025-03-22T23:51:20.538Z (about 1 year ago)
- Topics: accelerometer, accelerometry, actigraphy, data-analysis, sensors, sleep
- Language: Jupyter Notebook
- Homepage:
- Size: 136 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
Awesome Lists containing this project
README
==========================================================================================================================================
Code and materials from "Discrimination of sleep and wake periods from a hip-worn raw acceleration sensor using recurrent neural networks"
==========================================================================================================================================
This repository contains the documentation, code and materials for our paper
"Discrimination of sleep and wake periods from a hip-worn raw acceleration
sensor using recurrent neural networks".
The documentation contains code snippets to load the ``.gt3x`` and the data
from the Actiwave ``.edf`` files and store the data for the subjects that worn
both devices in a new HDF5 file ``BEDTIME_TU7.hdf5`` which is compatible with
the pandas API. Later notebooks also show how the manual annotation data can be
added resulting in a new ``ANNOTATED_BEDTIME_TU7.hdf5`` file.
Further notebooks can be found in which the performed experiments are documented
and in which the data analysis was conducted.
All code here was developed using conda to make it reproducible. To work with
this repository follow the following steps.
Getting Started
===============
1. Clone this repository
.. code-block:: bash
git clone https://github.com/Trybnetic/sleep-study.git
cd sleep-study/
2. Install the dependencies
.. code-block:: bash
conda create -f environment.yml
3. Activate the environment
.. code-block:: bash
conda activate bedtime
4. Work on the notebooks
.. code-block:: bash
jupyter notebook
Acknowledgements
================
This work was supported by the High North Population Studies at UiT The Arctic
University of Norway.