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

Dependencies include: unix, java 8 (Java 8 JDK) and python 3.7 (Anaconda's Python 3 or installation via Brew should do the trick).

$ git clone https://github.com/activityMonitoring/biobankAccelerometerAnalysis.git
$ cd biobankAccelerometerAnalysis
$ bash utilities/downloadDataModels.sh # Downloads example data and models for behaviour classification
$ pip install --upgrade pip # Upgrades pip version if required
$ pip3 install --upgrade -r requirements.txt # Installs a known working set of dependencies, other package versions may also work
$ javac -cp java/JTransforms-3.1-with-dependencies.jar java/*.java # Compiles Java code
$ 
$ # Now to install the package, run: 
$ pip3 install --user . 

Note for developers: If you are actively developing the package, you may wish to skip the installation step.

Keeping up to date

biobankAccelerometerAnalysis is regularly updated (e.g. a new dependency was introduced in January 2021, making the models compatible with the newest versions of dependency packages). To install the most recent version with the most recent set of dependencies:

$ git pull
$ cd biobankAccelerometerAnalysis
$ bash utilities/downloadDataModels.sh # Downloads example data and models for behaviour classification
$ pip install --upgrade pip # Upgrades pip version if required
$ pip3 install --upgrade -r requirements.txt # Installs a known working set of dependencies, other package versions may also work
$ javac -cp java/JTransforms-3.1-with-dependencies.jar java/*.java # Compiles Java code
$ 
$ # Now to install the package, run: 
$ pip3 install --user . 

Usage

To extract a summary of movement (average sample vector magnitude) and (non)wear time from raw Axivity .CWA (or gzipped .cwa.gz) accelerometer files:

$ python3 accProcess.py data/sample.cwa.gz
 <output written to data/sample-outputSummary.json>
 <time series output written to data/sample-timeSeries.csv.gz>

The main JSON output 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:

$ python3 accPlot.py data/sample-timeSeries.csv.gz
 <output plot written to data/sample-timeSeries-plot.png>

The underlying modules can also be called in custom python scripts:

    from accelerometer import summariseEpoch
    summary = {}
    epochData, labels = summariseEpoch.getActivitySummary("sample-epoch.csv.gz", 
            "sample-nonWear.csv.gz", summary)
    # <nonWear file written to "sample-nonWear.csv.gz" and dict "summary" updated
    # with outcomes>

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. Click here for detailed information on the data processing methods on our wiki.

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 [Walmsley2021]

[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

[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

[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

[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

This project is released under a BSD 2-Clause Licence (see LICENCE file)