ActiNet

A tool to extract meaningful health information from large accelerometer datasets. The software generates time-series and summary metrics useful for answering key questions such as how much time is spent in sleep, sedentary behaviour, or doing physical activity.

Installation

Minimum requirements: Python>=3.9, Java 8 (1.8)

The following instructions make use of Anaconda to meet the minimum requirements:

  1. Download & install Miniconda (light-weight version of Anaconda).

  2. (Windows) Once installed, launch the Anaconda Prompt.

  3. Create a virtual environment:

    conda create -n actinet python=3.9 openjdk pip

  4. Activate the environment:

    conda activate actinet

  5. Install actinet:

    pip install actinet

The next time you want to use actinet, just activate the environment again with:

conda activate actinet

Getting started

To extract and visualise a summary of activity data from a single file, run the following command in the terminal

actinet data/sample.cwa.gz -p

The sample-outputSummary.json will look like

{
    "Filename": "data/sample.cwa.gz",
    "Filesize(MB)": 69.4,
    "WearTime(days)": 5.948125,
    "NonwearTime(days)": 0.0,
    "ReadOK": 1,
    ...
}

And the timeSeries-plot.png will look like:

_images/samplePlot.png

Output plot of overall activity and class predictions for each 30-second time window

Citing our work

When using this tool, please consider citing the works listed in CITATION.md.

Licence

See LICENSE.md.

Acknowledgements

We would like to thank all our code contributors, manuscript co-authors, and research participants for their help in making this work possible. Contributors Graph.

Contents:

Indices and tables