MCPcopy Index your code
hub / github.com/OxWearables/biobankAccelerometerAnalysis

github.com/OxWearables/biobankAccelerometerAnalysis @v7.5.0

Chat with this repo
repository ↗ · DeepWiki ↗ · release v7.5.0 ↗ · + Follow
290 symbols 798 edges 33 files 145 documented · 50% updated 8mo agov7.5.0 · 2025-11-09★ 24820 open issues
What it actually does AI analysis from the code graph — generated when you open this
loading…
README

Accelerometer data processing overview

Github all releases DOI install

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.

Install

Minimum requirements: Python 3.7 to 3.10, Java 8 (1.8)

The following instructions make use of Anaconda to meet the minimum requirements:

  1. Download & install Miniconda (light-weight version of Anaconda).
  2. (Windows) Once installed, launch the Anaconda Prompt.
  3. Create a virtual environment: console $ conda create -n accelerometer python=3.9 openjdk pip This creates a virtual environment called accelerometer with Python version 3.9, OpenJDK, and Pip.
  4. Activate the environment: console $ conda activate accelerometer You should now see (accelerometer) written in front of your prompt.
  5. Install accelerometer: console $ pip install accelerometer

You are all set! The next time that you want to use accelerometer, open the Anaconda Prompt and activate the environment (step 4). If you see (accelerometer) in front of your prompt, you are ready to go!

Usage

Processing a single file

To extract summary movement statistics from an Axivity file (.cwa):

$ accProcess data/sample.cwa.gz

 <output written to outputs/sample/sample-summary.json>
 <time series output written to outputs/sample/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.

Batch processing multiple files

To process multiple files in a folder, you must specify which file extensions to process:

$ accProcess data/folder_with_cwa_files/ --fileExtensions cwa

 <outputs written to outputs/file1/, outputs/file2/, etc.>

You can specify multiple extensions (comma-separated):

$ accProcess data/my_data/ --fileExtensions cwa,bin,csv

The tool automatically includes compressed versions (.gz, .zip, .bz2, .xz), so --fileExtensions cwa will match both .cwa and .cwa.gz files.

To search subdirectories recursively:

$ accProcess data/my_data/ --fileExtensions cwa --recursive True

The batch processor will: - Automatically discover matching files - Process them serially with full error handling - Continue processing even if individual files fail - Provide a detailed summary of successful and failed files

To plot the activity profile:

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

Time series plot

Troubleshooting

Some systems may face issues with Java when running the script. If this is your case, try fixing OpenJDK to version 8:

$ conda install -n accelerometer openjdk=8

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.

Accelerometer data processing overview Activity classification

See Methods for more details.

Citing our work

When using this tool, please consider the works listed in CITATION.md.

Licence

See LICENSE.md.

Acknowledgements

We would like to thank all our code contributors and manuscript co-authors.

Contributors Graph

Core symbols most depended-on inside this repo

Shape

Method 145
Function 112
Class 31
Route 2

Languages

Python57%
Java43%

Modules by API surface

versioneer.py50 symbols
utilities/gui.py30 symbols
src/accelerometer/_version.py24 symbols
src/accelerometer/java/Features.java21 symbols
src/accelerometer/java/AccStats.java18 symbols
src/accelerometer/java/ActigraphReader.java16 symbols
src/accelerometer/classification.py14 symbols
src/accelerometer/utils.py12 symbols
src/accelerometer/java/EpochWriter.java12 symbols
src/accelerometer/java/DeviceReader.java11 symbols
src/accelerometer/device.py10 symbols
src/accelerometer/java/NpyWriter.java9 symbols

For agents

$ claude mcp add biobankAccelerometerAnalysis \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact

Ask about this repo answers extend the page