Interpreting state-of-the-art accelerometer monitoring of physical activity and sleep to reduce obesity and improve child health
Padaco is data visualization and analysis tool for accelerometry data obtained from a wearable device. It currently supports Actigraph's GT3X+ sensor. You can use the software to visualize this sensor data, which includes tri-axial accelerometry (X,Y,Z axis and their vector magnitude), luminance, and some other derived measures (e.g. step count and inclination).
See the home page at http://web.stanford.edu/~hyatt4/software/padaco for a user manual, test data set, and compiled versions of the software (Mac OS X only at the moment).
If you want to run the source code from here, then you will need to use MATLAB.
The software provides several visualizations of the data and clustering features when you have many days of recorded activity. There is an activity and sleep classification algorithm as well, which is described in the help section of the software.
See the license.txt file for licensing information.
This work was partially funded by a Stanford-Oxford Big Data for Human Health seed grant from the Li Ka Shing Foundation and a grant from the Stanford Child Health Research Institute and a grant from the Food and Drug Administration.