actinet 0.0.dev0

Activity detection algorithm compatible with the UK Biobank Accelerometer Dataset

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. The backbone of this repository is a self-supervised Resnet18 model.

Install

Minimum requirements: Python>=3.8, 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
   

   This creates a virtual environment called actinet with Python version 3.9, OpenJDK, and Pip.
4. Activate the environment:

   $ conda activate actinet
   

   You should now see (actinet) written in front of your prompt.
5. Install actinet:

   $ pip install actinet
   

You are all set! The next time that you want to use actinet, open the Anaconda Prompt and activate the environment (step 4). If you see (actinet) in front of your prompt, you are ready to go!

Usage

# Process an AX3 file
$ actinet sample.cwa

# Or an ActiGraph file
$ actinet sample.gt3x

# Or a GENEActiv file
$ actinet sample.bin

# Or a CSV file (see data format below)
$ actinet sample.csv

Troubleshooting

Some systems may face issues with Java when running the script. If this is your case, try fixing OpenJDK to version 8:

$ conda install -n actinet openjdk=8

Output files

By default, output files will be stored in a folder named after the input file, outputs/{filename}/, created in the current working directory. You can change the output path with the -o flag:

$ actinet sample.cwa -o /path/to/some/folder/

The following output files are created:

• Info.json Summary info, as shown above.
• timeSeries.csv Raw time-series of activity levels

See Data Dictionary for the list of output variables.

Crude vs. Adjusted Estimates

Adjusted estimates are provided that account for missing data. Missing values in the time-series are imputed with the mean of the same timepoint of other available days. For adjusted totals and daily statistics, 24h multiples are needed and will be imputed if necessary. Estimates will be NaN where data is still missing after imputation.

Processing CSV files

If a CSV file is provided, it must have the following header: time, x, y, z.

Example:

time,x,y,z
2013-10-21 10:00:08.000,-0.078923,0.396706,0.917759
2013-10-21 10:00:08.010,-0.094370,0.381479,0.933580
2013-10-21 10:00:08.020,-0.094370,0.366252,0.901938
2013-10-21 10:00:08.030,-0.078923,0.411933,0.901938
...

Processing multiple files

Windows

To process multiple files you can create a text file in Notepad which includes one line for each file you wish to process, as shown below for file1.cwa, file2.cwa, and file2.cwa.

Example text file commands.txt:

actinet file1.cwa &
actinet file2.cwa &
actinet file3.cwa 
:END

Once this file is created, run cmd < commands.txt from the terminal.

Linux

Create a file command.sh with:

actinet file1.cwa
actinet file2.cwa
actinet file3.cwa

Then, run bash command.sh from the terminal.

Collating outputs

A utility script is provided to collate outputs from multiple runs:

$ actinet-collate-outputs outputs/

This will collate all *-Info.json files found in outputs/ and generate a CSV file.

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.

Sample PyPI package + GitHub Actions + Versioneer

This template aims to automate the tedious and error-prone steps of tagging/versioning, building and publishing new package versions. This is achieved by syncing git tags and versions with Versioneer, and automating the build and release with GitHub Actions, so that publishing a new version is as painless as:

$ git tag vX.Y.Z && git push --tags

The following guide assumes familiarity with setuptools and PyPI. For an introduction to Python packaging, see the references at the bottom.

How to use this template

1. Click on the Use this template button to get a copy of this repository.

2. Rename src/sample_package folder to your package name — src/ is where your package must reside.

3. Go through each of the following files and rename all instances of sample-package or sample_package to your package name. Also update the package information such as author names, URLs, etc.

   1. setup.py
   2. pyproject.toml
   3. __init__.py

4. Install versioneer and tomli, and run versioneer:

   $ pip install tomli
   $ pip install versioneer
   $ versioneer install
   

   Then commit the changes produced by versioneer. See here to learn more.

5. Setup your PyPI credentials. See the section Saving credentials on Github of this guide. You should use the variable names TEST_PYPI_API_TOKEN and PYPI_API_TOKEN for the TestPyPI and PyPI tokens, respectively. See .github/workflows/release.yaml.

You are all set! It should now be possible to run git tag vX.Y.Z && git push --tags to automatically version, build and publish a new release to PyPI.

Finally, it is a good idea to configure tag protection rules in your repository.

References

• Python packaging guide: https://packaging.python.org/en/latest/tutorials/packaging-projects/
  • ...and how things are changing: https://snarky.ca/what-the-heck-is-pyproject-toml/ — in particular, note that while pyproject.toml seems to be the future, currently Versioneer still depends on setup.py.
• Versioneer: https://github.com/python-versioneer/python-versioneer
• GitHub Actions: https://docs.github.com/en/actions