NSRR Bootcamp Series: Powering Up Actigraphy Analyses with pyActigraphy

Grégory Hammad, PhD
GIGA - CRC In vivo Imaging, University of Liège, Liège, Belgium, Faculty of Medicine, Technical University of Munich, Munich, Germany

Overview: This workshop will guide users through the various actigraphy analysis steps; from data cleaning, batch computation of rest-activity rhythm variables to automatic rest episode detection.

Goals:
To read and visualize actigraphy recordings
To perform the necessary data cleaning steps
To compute rest-activity rhythm variables
To run multiple automatic rest detection algorithms and compute summary statistics
Resources:

Online documentation: https://ghammad.github.io/pyActigraph...
Git repository: https://github.com/ghammad/pyActigraphy
Paper: Hammad G, Reyt M, Beliy N, Baillet M, Deantoni M, Lesoinne A, et al. (2021) pyActigraphy: Open-source python package for actigraphy data visualization and analysis. PLoS Comput Biol 17(10): e1009514. https://doi.org/10.1371/journal.pcbi....

Prerequisites: Jupyter Notebook and python (3.6-3.10). Numpy and pandas. Additional packages will be installed automatically as dependencies of pyActigraphy.