After taking OPIM 3222 I learned about this really cool big data platform called Splunk. I learned that the big selling point of Splunk is that it can take any machine data and restructure it so that it is actually useful. So after I got a Fitbit Charge HR my first thought was, “I wonder what we can find if we Splunk this.” I worked with Professor Ryan O’Connor on this project, and after several hiccups, we finally got a working add-on. Back when we first started this project we found a “Fitbit Add-On” in Splunkbase that we just had to install and then we would be ready to go. After spending a lot of time trying to get this add-on set up we learned that it was a bit outdated and had some bugs that were making it quite difficult to use. After a while this project got pushed off to the side as we worked on other IoT related projects in the OPIM Gladstein Lab.
By the time another spark of inspiration came along, the Splunk add-on was gone because of its age and bugs. Since the add-on was gone we had to take matters into our own hands. Professor O’Connor and I split the work so that I would work on using the Fitbit API to pull data and he would then work on putting it into Splunk. I wrote Python scripts to collect data on steps, sleep, and heart rate levels. We then found that the Fitbit API required OAuth2 authentication every few hours to be able to continuously pull data. Professor O’Connor already tackled a similar issue when making his Nest add-on for Splunk. He used a Lambda function from Amazon Web Services to process this OAuth2 request. We decided to use the same function for the Fitbit, but the major difference is that the function is called every few hours. Professor O’Connor then made a simple interface for users to get their API tokens and setup the add-on in minutes. We then took a look at all of the data we had and decided the best thing that we could make was a step challenge. We invited our friends and family to track their steps with us and create a dashboard to summarize activity levels, create an all-time leaderboard, and visualize steps taken over the course of the day. However, this app only scratches the surface of what can be done with Fitbit data. The possibilities are endless from both a business and research perspective. We have already gotten a lot of support from the Splunk developer community and we are excited to see what people can do with this add-on.
By: Tyler Lauretti, Senior MIS Major