As one of the assignments of the Data Analysis Coursera class, participants were asked to detect movement type from the sensor data of a smartphone. When I did the assignment, I created the report in Markdown, and converted it using Pandoc. This process was much like I described in an earlier post.
Recently, Anneleen Maelfeyt has created a document style and logo for texts by Data Intuitive which I converted to a LaTeX template. As a proof of concept, I compiled the Markdown file with the new template. If you’re reading on a screen (including tablets), use this version: ActivityMonitoring-Screen. For printing, there’s a version with a modified cover and A4 paper size: ActivityMonitoring-Print.
An excerpt from the introduction in Markdown format:
Smartphones are ubiquitous, partly because they enhance our lives in many ways. Most recent smartphones have a so-called accelerometer[^1]. This device measure acceleration in 3 directions and in some cases also rotation by means of a gyroscope[^1a]. A relatively recent phenomenon is that smartphones (or the applications that run on them) attempt to be aware of the user *context* in the broad sense: location, movement, mood, schedule for the day, etc[^2]. One aspect of detecting the context of the user is movement and activity. Detecting whether someone is walking or sitting down makes a big difference for all sort of things: interests, way of interacting with the device (only thumb or more fingers?), etc.[^3] [^1]: <http://en.wikipedia.org/wiki/Accelerometer> [^1a]: <http://en.wikipedia.org/wiki/Gyroscope> [^2]: <http://www.forbes.com/sites/shelisrael/2012/07/17/ announcing-age-of-context-a-new-book-with-robert-scoble/> [^3]: Davies, N., Siewiorek, D.P., Sukthankar, R.: Activity-based computing. IEEE Pervasive Computing 7(2) (April 2008) 20-21
By the way, the Data Analysis course runs again starting the end of October. I highly recommend it.