GGIR

Converts raw data from wearables into insightful reports for researchers investigating human daily physical activity and sleep.

120
mentions
7
contributors

Cite this software

DOI:

10.5281/zenodo.1051064

What GGIR can do for you

  • GGIR is an R-package to process and analysis multi-day data collected with wearable raw data accelerometers for physical activity and sleep research.
  • GGIR uses this information to describe the data per day of measurement or per measurement, including estimates of physical activity, inactivity, and sleep. As part of the pipeline GGIR performs automatic signal calibration, detection of sustained abnormally high values, detection of sensor non-wear and calculation of average magnitude acceleration based on a variety of metrics.
  • GGIR is the only open source licensed software that provides a full pipeline for both physical activity and sleep analyses, with a high freedom for the user to configure the analyses to their needs.
  • The package has been used for domain science in 70+ publications, and is supported by 8 methodological publications.

The package has been developed and tested for binary data from GENEActiv and GENEA devices, .csv-export data from Actigraph devices, and .cwa and .wav-format data from Axivity. These devices are currently widely used in research on human daily physical activity.

A list of publications using GGIR can be found here: https://github.com/wadpac/GGIR/wiki/Publication-list

The package vignette which gives a general introduction can be found here: https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html.

Keywords
  • Big data
Programming language
  • R 100%
License
  • LGPL-2.0
</>Source code

Participating organisations

Activinsights Ltd
Inserm
MRC Epidemiology Unit
Netherlands eScience Center
University College London
University of Granada
University of Leicester

Mentions

Testimonials

Thank you @vtvanhees for your work and support on the #GGIRpackage
Damien Bachasson, Institute of Myology, Paris, France
The GGIR R package has been used extensively with GENEActiv, ActiGraph, and Axivity data and has grown organically to become the application of choice for many researchers using raw acceleration data to study not only PA and sedentary time, but also sleep.
Prof. Stuart Fairclough, Edgehill University, Ormskirk, UK

Contributors

Contact person

Vincent van Hees

Vincent van Hees

Netherlands eScience Center
Mail Vincent
EM
Evgeny Mirkes
University of Leicester
JM
Jairo Migueles
University of Granada
JHZ
Jing Hua Zhao
MRC Epidemiology Unit
JH
Joe Heywood
University College London
SS
Séverine Sabia
Inserm, University College London
Vincent van Hees
Vincent van Hees
Netherlands eScience Center
ZF
Zhou Fang
Activinsights Ltd

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