Fidgie Qlickr

Fidget away!

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Inspiration

After noticing the increased popularity of the fidget spinner we wanted to determine who it was marketed towards. At a quick glance of fidget spinner reviews, we noticed that users report an increase in attentiveness and focus and a decrease in disruptive coping mechanisms. We wanted to receive data to help reliably determine whether the two had any correlation. Upon further research we found that many people who experience ADHD/ADD/anxiety often perform better in cognitive tasks when doing repetitive motions such as fidgeting. Our hope for Fidgie Qlickr was to allow users to gain insightful data about their fidget toy use that could benefit users either by satisfying their curiosity, improve treatment plans with their mental health care professional and/or use the app as a tool to self regulate their emotions and behavior. We want to see if any positives would arise from collecting data either for the individual user or for the larger community through research, data collection and conclusive responses.

What it does

The Fidgie Qlickr is meant to help people who experience ADHD/ADD/anxiety to use less disruptive coping mechanisms to help bring them to a state of focus while also recording data. The Fidgie Qlickr has two components, the physical toy itself and the app that collects data. The physical model has features such as buttons, a joystick and a slider, which can be used for repetitive actions that help the user go into a more focused state. The app collects data from the user to track their emotions and usage of the Fidgie Qickr. With the collected data and input from the user, the app is able to display past and current records which could be used for the user to track or optionally be sent to a mental health physician for personalized treatment.

How we built it

The initial physical prototype of the Fidgie Qlickr was modeled on Solidworks to give us an idea of which components were necessary and could best collect data. Then using Tinkercad, we were able to model what it would be like to use the ESP32 to send data via WiFi from the Fidgie Qlickr physical toy to a Web Server to be analyzed. We converted the data into a text file so that we could analyze and model the data into graphs using MATLAB. In our development stages, we used Figma to create a basis for what we wanted the app to be able to do, such as displaying a visualization of the hours spent fidgeting. After finalizing the setup of the app in terms of the information we wanted to receive and the data we wanted to display, we moved onto creating an app that would be accessible to users. The app Fidgie Qlickr was coded using JavaScript in React Native, which allows the app to be used on iOS, Android and on the web.

Challenges we ran into

We had so many ideas planned out for Fidgie Qlickr, unfortunately we just did not have enough time to accomplish it all. Since we are a multidisciplinary team, we wanted to not solely focus on software, rather we wanted to utilize our mechanical and hardware skills to create an IoT project that consisted of mechanical, hardware and software components. For many of our team members it was their first time using MATLAB, let alone creating an entire app from scratch. We were able to model data collected on Tinkercad that could be analyzed in MATLAB; however due to time constraints, we were not able to directly incorporate the analyzed data in forms of graphs into the app. Additionally, we had trouble connecting the ESP32 board to a WiFi network. We were an in-person team, and due to security reasons we were not able to connect the ESP32 to the university WiFi network.

Accomplishments that we're proud of

We are extremely pleased that throughout this hackathon journey, we were able to learn and develop so many skills through MATLAB and React Native. We were delighted that we were able to have a complete prototype for the app on Figma. Even more so, we were elated that the app created on React Native had components that were functional and running smoothly.

What we learned

We are a beginner team, hence most of the software was new to the majority of us. We gained technical skills in learning how to code an app in React, analyzing data using MATLAB, prototyping an app using Figma and setting up a webserver for ESP32 wireless communication. Moreover, We learned to collaborate efficiently. We were quick to identify where our individual technical skills lay and we divided the tasks accordingly. We had a lot of fun making the pitch video as well!

What's next for Fidgie Qlickr

Coming up soon for Fidgie Qlickr is integrating the data from MATLAB into the Fidgie Qlickr app. Another feature to look forward to would be an optional guided calm session. This would be executed using repeated dimming cycles of LEDs on the toy itself, to relieve stress in certain situations.

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