Monthly Archives: October 2016

OpenLabyrinth stress testing at CHES scholarship day

On Wed, 5th October, the Centre for Health Education Scholarship (CHES) at UBC held its annual scholarship symposium, in Vancouver.

There were many interesting sessions, including a stirring keynote address from Rachel Ellaway (Professor, Education Research, University of Calgary.

OpenLabyrinth featured at a few presentations at the CHES symposium, including a short presentation on Activity Metrics by David Topps and Corey Albersworth. (SeeĀ http://www.slideshare.net/topps/activity-metrics-for-ches-day )

In one of the afternoon demonstration sessions, we were able to show our Arduino stress-detector kit in action to conference participants. Here we have a short video of the Arduino sensors being calibrated.

This was the same basic setup as that first shown at the Medbiq Conference in Baltimore earlier this year. However, for this conference, no expense was spared. We splurged another $29.99 on another Arduino device. Yes, it nearly broke the budget!

We also managed to set up the software on both Windows 10 and OS X Yosemite, which highlights the platform independence of the Eclipse IDE that we used for collecting the Arduino data and sending it to the LRS.

Here we have a short video of the OpenLabyrinth stress-test in action. Our participant is playing a rapid-fire series of case vignettes on the Mac on the right, while the Arduino sensors connected to the Windows machine on the right is recording real-time data on her heart rate and Galvanic Skin Response.

We initially created this project as a simple technical demonstration that one could use a cheap, easy combination of Arduino hardware, OpenLabyrinth and xAPI statement collection into the GrassBlade Learning Record Store. We had only intended to show that such collection from multiple activity streams was feasible in the time and resources available to the average education researcher i.e. not much.

We were delighted to find that the stress detector was much more sensitive than we anticipated and will be useful in real-world research.