
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
$ pip install accelerometer
You will also need Java 8 (1.8.0) or greater. Check with the following:
$ java -version
You 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.gz
Getting started
To extract a summary of movement (average sample vector magnitude) and (non)wear time from raw Axivity .CWA (or gzipped .cwa.gz) accelerometer files:
$ accProcess data/sample.cwa.gz
<output written to data/sample-outputSummary.json>
<time series output written to data/sample-timeSeries.csv.gz>
The main output JSON will look like:
{
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
}
To visualise the time series and activity classification output:
$ accPlot data/sample-timeSeries.csv.gz
<output plot written to data/sample-plot.png>

Output plot of overall activity and class predictions for each 30sec time window
Citing our work
When describing or using the UK Biobank accelerometer dataset, or using this tool to extract overall activity from your accelerometer data, please cite [Doherty2017].
When using this tool to extract sleep duration and physical activity behaviours from your accelerometer data, please cite [Willetts2018] [Doherty2018] and [Walmsley2020].
- Doherty2017
Doherty A, Jackson D, Hammerla N, et al. (2017) Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLOS ONE. 12(2):e0169649
- Willetts2018
Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961
- Doherty2018
Doherty A, Smith-Bryne K, Ferreira T, et al. (2018) GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nature Communications. 9(1):5257
- Walmsley2020
Walmsley R, Chan S, et al. (2020) Reallocating time from machine-learned sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk (preprint https://doi.org/10.1101/2020.11.10.20227769)
Licence
This project is released under a BSD 2-Clause Licence (see LICENCE file).
Contributors
See https://github.com/activityMonitoring/biobankAccelerometerAnalysis/graphs/contributors
Contents: