julienlhk

julienlhk / whoop

Public

Independent BLE research on WHOOP 5.0: docs + Python tools to stream standard heart rate & R-R intervals from your own strap. Educational and interoperability research only — not affiliated with WHOOP, Inc.

16
1
75% credibility
Found Jun 02, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is an educational research project that helps people understand how WHOOP 5.0 fitness trackers communicate over Bluetooth. It provides simple tools to scan for your WHOOP device, connect to it, and stream live heart rate data along with beat-to-beat timing information (used for recovery metrics). The project focuses on standard Bluetooth protocols that are openly documented, and includes research notes about a proprietary protocol that remains locked after pairing with the official app. Everything is designed for learning about wireless health data and working only with devices you own.

How It Works

1
🔬 You hear about this research project

You discover a community project that helps people understand how their WHOOP fitness tracker communicates over Bluetooth, designed purely for learning about health data and wireless technology.

2
💻 You set up your computer

You install Python on your Mac and prepare to explore how your WHOOP sends data wirelessly, using tools that work with your computer's Bluetooth connection.

3
🔍 You find your WHOOP device

You run a scan that searches for nearby WHOOP straps broadcasting their presence, and your specific device appears in the list with its unique identifier.

4
📡 You connect and start listening

Your computer establishes a direct wireless link to your WHOOP strap and subscribes to receive live updates about your heartbeat as it happens.

5
❤️ Your heart rate appears on screen

Each heartbeat arrives in real-time showing your current beats per minute and the precise timing between beats (which reveals your recovery state), printed neatly as they happen.

6
📊 You review your session data

After your capture session ends, you run a simple analysis that calculates your minimum and maximum heart rate, your average, and a key recovery metric called RMSSD from your beat-to-beat intervals.

✅ You have your health data

You've successfully captured and analyzed your personal heart health data from your own WHOOP strap, all on your own computer, ready to build your own insights or health tools.

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AI-Generated Review

What is whoop?

This is an independent research project that cracks open WHOOP 5.0's Bluetooth protocol so you can pull heart rate and R-R interval data straight from your strap—no WHOOP app required. It runs on Python, uses the bleak BLE library, and gives you a small command-line toolkit: scan for nearby straps, stream live BPM and beat-to-beat intervals, read battery and firmware info, and save everything to JSONL for offline analysis. The project also documents the proprietary fd4b protocol path, though that remains blocked on macOS after official app pairing.

Why is it gaining traction?

WHOOP's ecosystem is notoriously closed—you rent your own biometric data back to yourself through their subscription model. This project is a direct challenge to that lock-in, giving developers a working path to raw sensor data using standard Bluetooth GATT. The standard heart rate path is proven and stable; the real hook is the R-R intervals, which let you calculate HRV metrics like RMSSD without WHOOP's proprietary algorithms. For the research community, it's also a rare window into how fitness wearables actually communicate over the air.

Who should use this?

Quantified-self enthusiasts who want to own their biometric data. Researchers studying heart rate variability or building custom health pipelines. Developers exploring BLE reverse engineering as a learning exercise. If you're expecting a polished product with support, look elsewhere—this is academic tooling for hardware you already own.

Verdict

With 16 stars and a 0.75% credibility score, this is early-stage, niche research—solid for experimentation but not production-ready. The documentation is thorough and the core HR/RR streaming works, making it a worthwhile starting point if you're curious about wearable data extraction. Just don't mistake it for a finished product.

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