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https://youtu.be/46FgKMKWEvY

Inspiration

Overheard at Penn - “I just wasted 2 hours, trying to find a quiet place to study.” This poor freshman cried all night completing his assignment at McClelland Cafe. But to provide you with a better choice, we present to you the “Study Space Tracker.” Next time, don’t worry about where to study, ask “Study Space Tracker” for the best spot - less noisy, and not closed!

1 minute Promotional Video - https://bit.ly/2PVDQBM

Abstract

To determine the ideal study spot, the Study Space Tracker gathers real-time data from a light dependent resistor (LDR) and a microphone sensor to measure the light intensities and sound levels of a study area. The ideal conditions are that there is enough light to study and the noise levels are minimal. If the room is not available, a red LED lights up and a message reporting that is sent to the user via Twilio. Whenever the LED turns off there will be a message sent, saying the room is available.

How we built it?

The initial testing was done by setting up simple Arduinos to take in microphone and LDR readings. After many iterations of data collection, we determined the minimum LDR value to be 40 units, in order for the room to well lit, and the microphone values to be no more the 700 units, in order for the room to be quiet enough.

An MKR 1000 was used in each study space being tracked. It gathers real-time data of both noise levels and light intensity levels. With the thresholds levels set, a boolean called roomAvailable is turned to true or false. This triggers a message to be sent to Thingspeak over a stable wifi connection. ThingspeakHTTP then sends a trigger to Twilio to send a message from the Twilio phone number to your chosen phone number.

The LDR determines an overarching logic to see if the space is even open at that point. This was designed for spaces like libraries that close at midnight, or lobbies where lights are dimmed after sunset. If the space is not even open, our systems don’t even look for a noise intensity; however, when the space is open, it checks to see if the noise levels are below the set thresholds.

Final Arduino MKR 1000 Code for each room and Twilio Setup - https://goo.gl/HApG8Y

Challenges we ran into

The biggest setbacks we entailed is when we were trying to connect our MKR1000, Thingspeak and Twilio. In order for us to get Twilio working, our first challenge was getting our Arduino connected to the wifi, which seemed like an impossible task after many unsuccessful attempts. We switched between using the MKR1000, Arduino Uno with WiFi shield, and Arduino Uno WiFi multiple times, and each time ran into problems with connections and sending data over a stable enough connection. We, finally, resolved to use the MKR1000 by getting it to connect to the internet via a portable hotspot and the Wifi-101 library.

Another setback came with the design of our replica study area. At first, our design to enlarge a smaller version of a previously made box failed because it did not all scale exactly how we intended it to, and the box did not fit together. To fix this, after multiple more attempts, we used our small box sides and the mirror command in SolidWorks to design our final box that fit together like a glove.

What we learned?

Over the long process of creating the Study Space Tracker, we learned how to use sensors to send data to Thingspeak, which furthers sends a trigger to Twilio to send an SMS to your phone. Just trying to connect MKR 1000 to Twilio helped us learn how to implement complex code to establish the connection to send specific messages under different circumstances. We also learned how to wire our sensors on a breadboard efficiently, and get the data that was required. We learned how to maximize our microphone sensor's accuracy by changing small things such as connecting it to three volts instead of five and turning the internal resistance down on the sensor itself. This hands-on project made it possible to learn a wide range of skills like working in a team.

What's next for Study Space Tracker?

Large institutions, like Penn, have a large number of students and lots of amazing spots to study and host discussions in. A product like Study Space Tracker can help students to choose the quietest spot to study in, instead of wasting time by physically going to a place only to find people playing air hockey or loud music.

Before this can be marketed directly to these institutions, the device has to be made more compact and robust. There is a need of complex logic to draw “studio” flowcharts on Twilio in order to have the right triggers send the right outputs. For instance, whenever a message like “Which room is available?” is received on Twilio phone number, reply with a list of spaces available once. These require more complex codes than individual devices sending different messages but would take this idea of a Study Space Tracker to the next level.

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