Seeing Like a Bike: Week 7

Our progress over the last week was fairly straightforward, and we are currently on track to have great results by the end of the program. We spent most of the last week without the GRIMM, so we didn’t go out to collect more data. However, we started preliminary analysis of the data we collected last Monday, testing to see if our method of mobile air quality sensing is feasible.

When the real-time clocks arrived, we connected them to the Raspberry Pi, and updated our code to take advantage of the new hardware. This was a very important step, as Pi’s themselves don’t contain a clock on-board, so when the Pi is powered off, it doesn’t actually record the passage of time, adding a significant layer of confusion onto our data collection.

In the meanwhile, Urvi has been designing a 3d printed box to place the Arduino and Air Quality sensor inside, which should hopefully mitigate the many problems we have been having with wiring. The biggest issue is simply that our current setup is only temporary, a few weeks at most, so we don’t want to take the step of soldering the wires to the Arduino. Instead, we are using hot-glue, and electrical tape, which is holding up decently, but not as reliably as we would like.

On Friday, I went out for the first time with two sensors simultaneously. Instead of going on our typical 2.5 mile route starting from Piedmont, I simply took a short loop around Georgia Tech’s campus. These results were promising, but one of our two sensors was giving us very inconsistent data, as around 10% of the time, it would just decide to not give us any data, and we couldn’t find a specific cause of this. Also on Friday, we got the GRIMM back, and to make up for lost time, I decided to go out again on Saturday to do more tests.

On Saturday, I spent the beginning part of the day setting up the entire system, and finalizing the hardware for the bike, and software for the Pi. After going for another quick ride around campus, I discovered that some of our wiring had failed on that run. The next few hours, I focused on rewiring the system, and ensuring that the system would be reliable and resistant to stress. On the second run that day, the setup worked perfectly! Additionally, the data we collected exceeded our expectations, as the two sensors seemed to be aligned perfectly with each other, and seemed to differentiate the various types of streets on the route very well. On the first three segments, the values mimicked each other, and on the 4th segment, the variance in values was significant, but easily explainable due to the positioning of the sensors on the bike (see above image), and the construction taking place on the route. In fact, we would be more worried if the data did match on the last segment!

After analyzing the data from Saturday, we went on another run today, to collect more data similar to our standard Piedmont route choice, as well as adding GPS data collected from a log on my phone. Tomorrow, we plan to have our final major data-collection day, as we will hold experiments not just on the feasibility of the system, but to collect actual, usable data! The rest of the week will be spent analyzing the data from tomorrow, and deciding our path for the last three weeks of the program.