FiLog

MockUP for mobile tracker device on patient's side.
Hardware setup (Arduino with buttons)
MockUP for patient's information chart on provider's side.

Inspiration

Real-time communication between patient and provider is often inadequate, leading to blind spots in treatment. For example, in hospitals and care settings, patients—especially children, stressed individuals, or those with communication barriers—struggle to express their needs clearly. This creates critical gaps in understanding their condition, resulting in potentially inaccurate assumptions, reduced quality of care, and unnecessary stress for both patients and staff. Current methods—such as verbal check-ins or rating scales with pictures and numbers—are inconsistent, biased by the moment, and poorly organized for long-term understanding.

What it does

FiLog (a combination of the words "Feel" and "Log") is a universal data tracker with app integration for real-time patient-provider communication. Patients log data (e.g., pain, mood, discomfort) instantly with a simple button press, and each entry is saved and tied to the patient’s profile. On the other end, providers receive a clear summary on a web dashboard: most recent state plus easy-to-read graphs showing trends over time. This transforms scattered, one-off check-ins into continuous, reliable communication.

How we built it

We used React for the frontend, Node.js for the backend, Socket for frontend and backend integration, and Arduino for button wiring and programming.

Challenges we ran into

We had some hardware troubles throughout this event, namely not having access to an esp32 and not being able to connect to the Spark Cores. We planned to use some hardware device that could connect to wifi in order to send an http request to our web server. However, with the resources we had access to, we were forced to adapt and plan around our situation. We ended up using a standard Arduino Uno, involving some more complex processes on the software side.

Accomplishments that we're proud of

The dashboard web app looks professional, and all the data is laid out in a clean UX style. And while we didn't end up using them, we 3D printed custom buttons to put on the Arduino for the three different moods. We're equally proud of persevering through challenging moments, quickly adapting to changing circumstances, and exploring the full scope of our chosen problem.

What we learned

We learned about the SparkCore microcontroller, which we considered as a contender for sending the Arduino IDE output to a local server. While we did not end up using the SparkCore, we learned a lot about it from debugging its functionality and relating its relevance to our project.

What's next for FiLog

Data from patients or users collected anonymously; can be sorted via conditions, ailments, ect… Data can be anonymously collected across patients to build a universal health database (due to more reliable and less biased data), improving research operations. Can be integrated with machine learning to identify patterns, and predict treatment outcomes, furthering progress in the medical field.