This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
Hey, do you also find it a hassle to carry your phone or health band around when going for a run, cycling, or just a simple walk?
Having something attached to your body while enjoying these pursuits feel uncomfortable. The phone bouncing in your pocket drags you behind, the sweat engulfs the health band and can't even let you wipe your forehead easily.
But you still want to track your workout session.
Don't worry, introducing the Carry Free Pedometer 🎉🎉. It lets you estimate your workout stats from your existing workout history. All you need is your workout duration and breaks in between.
Note: You need your Google Fit's Takeout .zip file for the first usage.
You can download it from here: https://takeout.google.com/
Demo
It is deployed on the web for hassle-free usage. The data leaves off your device. But the code is open and publicly available on GitHub.
Deployed on Render:
Code
Users can run this tool locally by following the instructions in the README.md file. No data will leave their device.
imayushfr
/
carry-free-pedometer
A tool that lets you estimate Google Fit's activity metrics (like heart minutes, calories, distance, and speed) without carrying your phone or health band around.
Carry Free Pedometer
A tool that lets you estimate Google Fit's activity metrics (like heart minutes, calories, distance, and speed ) without carrying your phone or health band around.
Language Used
- Python 3
Libraries Used
- NumPy
- Pandas
- Streamlit
- streamlit_local_storage
How to Run on Your Local Machine
1. Clone this Repository
git clone https://github.com/imayushfr/carry-free-pedometer.git
2. Navigate to Cloned Directory
cd carry-free-pedometer
3. Create a Virtual Environment
python -m venv venv
4. Activate the Virtual Environment
Windows:
venv\Scripts\activate
Linux / macOS:
source venv/bin/activate
5. Install Dependencies
pip install -r requirements.txt
6. Run the Application
streamlit run app.py
7. Open the Application
Streamlit will display a local URL in your terminal, usually:
http://localhost:8501
How to Use
-
Download your Google Fit's Takeout
.zipfile from here: https://takeout.google.com/ (You only need this for the first usage). -
Upload the
.ziptakeout file to the application. It will create a.jsonfile. -
Upload that
.json…
How I Built It
It is built using a lightweight tech stack with minimal features and code.
The tech stack includes: NumPy, Pandas, and Streamlit. Data processing and curve fitting are handled by pandas and numpy. UI and file I/O is handled by streamlit.
Why Does Open Innovation Matter?
Nobody wants their health data on someone's else server.
Open innovation is a boon for them. All the code for this tool is public on GitHub. People can run this tool locally by following the instructions in the README.md file. This way, their data will never leave their device.
People usually don't trust a closed API, especially for health data. This tool makes sure that people can see the code (as it is open) and don't even have to trust the uncertainty and security of their data.
My First User
Yupp, I landed my first user. She's my friend. Actually, the inspiration itself came from her. She used to complain about carrying her phone to the run, and also leaving her phone behind for the run (because now her run won't be tracked) 😂ðŸ˜.
I showed this tool to her, uploaded her takeout file, and boom! Now she can go around without her phone, hurrayyy! 🥳🥳
Her reaction was just wow. She said that she'd definitely going to use it, its just so easy. But she's not going to pay for it for now, she has no money 😔.
Prize Categories
Best Use of Render:
Deployed the streamlit app on Render, so that it can process large takeout files without having to set up the application locally.
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