accelerometer 5.1.2

A package to extract meaningful health information from large accelerometer datasets e.g. how much time individuals spend in sleep, sedentary behaviour, walking and moderate intensity physical activity

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 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

Usage

To 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 here for the list of output variables.

Actigraph and GENEActiv files are also supported, as well as custom CSV files. See the documentation for more details.

To visualise the activity profile:

$ accPlot data/sample-timeSeries.csv.gz
 <output plot written to data/sample-timeSeries-plot.png>

Under the hood

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.

Citing our work

When describing or using the UK Biobank accelerometer dataset, please cite [Doherty2017]. When using this tool to extract sleep duration and physical activity behaviours from your accelerometer data, please cite:

1. [Doherty2017] Doherty A, Jackson D, et al. (2017) Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLOS ONE. 12(2):e0169649

2. [Willetts2018] Willetts M, Hollowell S, et al. (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961

3. [Doherty2018] Doherty A, Smith-Byrne K, et al. (2018) GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nature Communications. 9(1):5257

4. [Walmsley2021] Walmsley R, Chan S, Smith-Byrne K, et al. (2021) Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease. British Journal of Sports Medicine. Published Online First. DOI: 10.1136/bjsports-2021-104050

Licence

See license before using this software.