Converts raw data from wearables into insightful reports for researchers investigating human daily physical activity and sleep.
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.
Thank you @vtvanhees for your work and support on the #GGIRpackage
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.
Advancing actigraphy-based daily activity and sleep analysis with machine learning
Detecting human sleep from wearable accelerometer data without the aid of sleep diaries
Gaining insights from wearable movement sensors