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.
pip install accelerometerYou also need Java 8 (1.8.0) or greater. Check with the following:
java -versionYou can try the following to check that everything works properly:
# Create an isolated environment
$ mkdir test_baa/ ; cd test_baa/
$ python -m venv baa
$ source baa/bin/activate
# Install and test
$ pip install accelerometer
$ wget -P data/ http://gas.ndph.ox.ac.uk/aidend/accModels/sample.cwa.gz # download a sample file
$ accProcess data/sample.cwa.gz
$ accPlot data/sample-timeSeries.csv.gzTo extract summary movement statistics from an Axivity file (.cwa):
$ accProcess data/sample.cwa.gz
<output written to data/sample-outputSummary.json>
<time series output written to data/sample-timeSeries.csv.gz>Movement statistics will be stored in a JSON file:
{
"file-name": "sample.cwa.gz",
"file-startTime": "2014-05-07 13:29:50",
"file-endTime": "2014-05-13 09:49:50",
"acc-overall-avg(mg)": 32.78149,
"wearTime-overall(days)": 5.8,
"nonWearTime-overall(days)": 0.04,
"quality-goodWearTime": 1
}See Data Dictionary for the list of output variables.
Actigraph and GENEActiv files are also supported, as well as custom CSV files. See Usage for more details.
To visualise the activity profile:
$ accPlot data/sample-timeSeries.csv.gz
<output plot written to data/sample-timeSeries-plot.png>
Interpreted levels of physical activity can vary, as many approaches can be taken to extract summary physical activity information from raw accelerometer data. To minimise error and bias, our tool uses published methods to calibrate, resample, and summarise the accelerometer data.
See Methods for more details.
When using this tool, please consider the works listed in CITATION.md.
See LICENSE.md.
We would like to thank all our code contributors and manuscript co-authors.