A tool to extract meaningful health information from large accelerometer datasets e.g. how much time individuals spend in sleep, sedentary behaviour, and physical activity.
To extract a summary of movement (average sample vector magnitude) and (non)wear time from raw Axivity .CWA accelerometer files:python ActivitySummary.py [input_file.CWA] [options]
python ActivitySummary.py sample.cwa
Click here for a sample .CWA file.
The output will look like:
{
"file-name": "sample.cwa",
"file-startTime": "2014-05-07 13:29:50",
"file-endTime": "2014-05-13 09:50:25",
"pa-overall-avg(mg)": "33.01",
"wearTime-overall(days)": "5.80",
"nonWearTime-overall(days)": "0.04"
}
Click here for customised usage options on our wiki.
Dependancies include: java and python (numpy and pandas).Click here for detailed information on installing this software on our wiki.
We are using a combination of published methods to extract meaningful health information from accelerometer data. [Clicke here for information on the data processing methods on our wiki](https://github.com/aidendoherty/biobankAccelerometerAnalysis/wiki/3.-Methods-Overview)
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