This is a repository for contextlogger3 software that is being built in Aalto University on top of the funf framework (http://code.google.com/p/funf-open-sensing-framework/). This project has originated from and is inspired by the ContextLogger2 project (http://contextlogger.org/) developed at Helsinki Institute for Information Technology (HIIT). All source code in this project is published under MIT open source license (http://opensource.org/licenses/MIT). If you have any questions about the project, please contact Kimmo Karhu (firstname.lastname [at] aalto.fi).
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Make a prototype of visualizing accelerometer data collected from contextlogger #10
Make a prototype of visualizing accelerometer data (x, y, z acceleration) over time axis. Make two prototypes using Google visualization framework and the other visualization framework that you are already familiar with.
Please note, that for time axis you need to use EVENT_TIMESTAMP column (measured in nanoseconds) and not the TIMESTAMP column (which is not accurate enough for sensor readings). Since EVENT_TIMESTAMP is not tied into real time (i.e start time is unknown) most likely you need to use TIMESTAMP column to tie the first measurement from sensor burst into real time (i.e. my example data is collected starting in the morning of Oct 17, around 8:30am)
For this visualization, use the same example data (Dropbox/Funf/kimmo_one_day.zip) as for the maps visualization.
Make a prototype of visualizing accelerometer data (x, y, z acceleration) over time axis. Make two prototypes using Google visualization framework and the other visualization framework that you are already familiar with.
Please note, that for time axis you need to use EVENT_TIMESTAMP column (measured in nanoseconds) and not the TIMESTAMP column (which is not accurate enough for sensor readings). Since EVENT_TIMESTAMP is not tied into real time (i.e start time is unknown) most likely you need to use TIMESTAMP column to tie the first measurement from sensor burst into real time (i.e. my example data is collected starting in the morning of Oct 17, around 8:30am)
For this visualization, use the same example data (Dropbox/Funf/kimmo_one_day.zip) as for the maps visualization.