Project Info

NeuraFlow

Inspiration

As all of us have come across Alzheimer's or dementia so far, we understand how difficult it is to lead a life in that way. When we looked at our family members or friends that deal with these problems, we were determined to use our time at TreeHacks to tackle this - and allow it to truly enhance people's everyday lives. Could we build Meta Glasses or something similar that can truly change the lives of these individuals? We are leaving this Hackathon knowing we just did this.

What it does

The NeuroFlow glasses provide dementia patients with an AI Healthcare Assistant that can provide them with any information about things or people they want to remember. It measures the heart rate of the patient, and if it notices distress, then it will ask the dementia patient how it can help. It has full context awareness of what the user likes for them to store on their behalf. The dementia patient can then easily recall whatever they are struggling to remember. With our implementation, it can even read and respond in 100+ languages!

How we built it

The hardware component of the product are 2 ESP32s that provide camera data and heart rate information. This data is analyzed to determine if an individual's heart rate is abnormal - in such a case we expect the dementia patient to be in need of help. This is when we activate our classification model that determines who a person is, providing helpful supplementary information to allow people to work. In the event that the dementia patient says, "Flow", our agent turns on to ask the user what they need help with. Access to their entire day's worth of information (and the internet too!) is at their disposal. In the event that they need some calm nature music, they can get that too!

Challenges we ran into

Some challenges we ran into were primarily related to connection. Learning how to parse the data provided by the ESP32 consistently with the WiFi faltering and our own novelty with the subject was especially difficult, but over time, we loved every minute of the challenges, and we learned so much! We also were trying to implement a CNN at first, but classifying many images of us through the grainy camera would have been difficult. To improve our efficiency, we applied FaceNet instead using embeddings.

Accomplishments we're proud of

Within 36 hours, we were able to build this entire project from 0 to 1. We started with the three of us and 2 ESP32, and we are leaving the Packard building with a solid voice recognition model for dementia patients that can not only register when they are stressed, but also recognize people around them and provide them with information that they need stored.

What we learned

That we can be a lot more ambitious nowadays. Each of us learned from each other - from the hardware to AI architecture to voice recognition and response models. We managed git conflicts, learned how to work under intense time pressure, and even how to CAD a sweet design.

What's next

We see ourselves scaling up our compute more, so this tool can be more assistive to more people with alzheimer's and dementia. We genuinely believe in this tool, and we think we can be used by many people that not only have dementia but could use a healthcare assistant at any given time. It truly can enhance human lives at a very minimal cost (literally fractions of a cent). We can also pick up on more biomarkers to be even more confident about how to respond with the in-built agent. Memory agents to parse through our vast amounts of data to personalize the user experience will also be a crucial next step.

NeuraFlow v2.0

A real-time biometric and vision analysis system with a modern React UI.

Features

❤️ Real-time Heart Rate: Visualization and spike detection via BLE chest strap.
📸 Live Computer Vision: Face recognition and person identification via ESP32-CAM.
🗣️ Flow Voice: Personalized greeting and interaction using OpenAI.
📊 Live Dashboard: Real-time graphing and system logs.

🚀 Setup Guide for Collaborators

1. Prerequisites

Python 3.9+
Node.js 18+
Hardware: ESP32-CAM, BLE Heart Rate Monitor (Polar/Garmin/etc.)

2. Clone the Repository

git clone <repository-url>
cd NeuraFlow

3. Backend Setup

Create a virtual environment and install dependencies:

# Create venv
python3 -m venv venv

# Activate venv
# On Mac/Linux:
source venv/bin/activate
# On Windows:
# .\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

4. Frontend Setup

Install Node.js dependencies:

cd frontend
npm install
cd ..

5. Configuration (.env)

Create a .env file in the root NeuraFlow/ directory. You can copy the structure below:

# NeuraFlow/.env

# ESP32 Camera IP (Get this from your serial monitor)
CAM_IP=192.168.1.X

# BLE Heart Rate Monitor UUID (Run 'python scan_ble.py' to find yours)
ADDR=YOUR-BLE-UUID-HERE

# OpenAI API Key (For voice features)
OPENAI_API_KEY=sk-your-key-here

6. Running the System

Step 1: Start the Backend Make sure your venv is activated.

python3 server.py
# Server will start on http://localhost:8000

Step 2: Start the Frontend Open a new terminal window.

cd frontend
npm run dev
# UI will open at http://localhost:5173

🛠 Troubleshooting

BLE Connection Failed: Ensure Bluetooth is on and run python3 scan_ble.py to verify your device is visible and get the correct UUID.
Camera not showing: Check if the ESP32 is powered on and the IP in .env matches.
"Module not found": Ensure you have activated the virtual environment (source venv/bin/activate) before running python scripts.

Analysis

Compare with all teams

View

Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

CIn code
C++In code
CSSIn code
FastAPIIn code
HTMLIn code
JavaScriptIn code
OpenAIIn code
PythonIn code
PyTorchIn code
ReactIn code
Tailwind CSSIn code

11 of 11 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

348 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.