MoodLoop

hardware system
Flask software
Blender model

Inspiration

As students, we often experience stress, anxiety, depression, or emotional ups and downs throughout the day. However, we rarely pause to check in with our internal emotional state. Because daily life is busy, students may forget to reflect on how they are feeling. This lack of self-awareness can make it harder to recognize patterns in stress, burnout, or depression over time. Additionally, therapists can prescribe this tool to patients struggling with mental illnesses especially with emotional dysregulation to help increase their emotional awareness.

What it does

Our solution is a Mood-Tracking Keychain, a small and discrete portable device that encourages users to check in with their emotions throughout the day. The keychain sends gentle reminders at a few scheduled times during the day. When prompted, the user can quickly log their mood using a single button. RGB LEDs displays the selected mood using color, and users can select the intensity of their selected emotion via buttons, making the interaction fast and intuitive. By combining timely reminders, and quick mood logging the device helps users build a habit of emotional awareness and self-reflection.

How we built it

The hardware components involve a EMF8 microcontroller with an embedded C program that controls the LEDs and Buttons. When the 'save' button is activated, the mood and intensity information is sent to the software via a serial port. This allows the Python to takes the information in real time and display it in visual 'All time' and 'Weekly' graphs. In this MoodLoop dashboard, users can also explore insights generated by Llama about their emotional trends and import, export, and delete their data. To improve portability, we designed the keychain model in Blender.

Challenges we ran into

There were some challenges we ran into, especially regarding which LLM to use (OpenAI, Gemini, Anthropic, etc.). We ended up successfully implementing Llama for insight generation through Huggingface. Additionally, integrating software and hardware communication through the serial port was a notable challenge as well.

Accomplishments that we're proud of

We're proud of learning how to use new tools, such as Figma, Blender, and Llama. Additionally we explored hardware for the first time and are proud that we took on this challenge.

What we learned

We learned technical skills in design and development, using a variety of tools. We also improved on our ability to collaborate and work together efficiently as a team.