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| Garmin smartwatches can receive data from an ESP32 board. |
For years, Garmin has built a reputation on a simple promise: if you want the best running dynamics, you buy our ecosystem. But one developer, armed with curiosity and an AI coding assistant, just proved that a $5 chip can trick a Garmin Fenix into treating a homemade sensor like a first-class citizen.
Garmin offers a comprehensive ecosystem. From the watch on your wrist to the heart rate strap on your chest, everything is designed to work together seamlessly. The catch? It is not an entirely open one, even though certain steps have been taken to make it more accessible. For example, users cannot just pair any sensor with a Garmin smartwatch to display advanced metrics such as running efficiency. This might be less relevant for everyday runners, but it is potentially significant for makers and hardware startups.
That wall may have just developed its first major crack.
The 'Impossible' Pairing
Enter Sam Dumont, a freelance cloud engineer from Belgium. Dumont has been tinkering with the Garmin platform since 2020, and he set out to solve a problem that has long frustrated the DIY community: making a Garmin watch accept data from a non-Garmin chest strap as if it were a native accessory.
Specifically, he wanted to stream running efficiency metrics—namely ground contact time and vertical oscillation—to a Garmin Fenix watch.
If you are a runner looking to track your performance in serious detail, the Garmin Fenix series ( check the latest models on Amazon ) is the gold standard. But like most gold standards, it is protective of its territory. Garmin’s protocols are undocumented, and the "good parts" of the data stream are locked to its own straps. To get a third-party sensor to display data on the native Garmin screen, you need workarounds the company never intended.
The Hack: Spoofing a $200 Strap with a $5 Chip
Dumont succeeded where many have failed. Using an ESP32 or nRF52832 chip (cheap, low-energy Bluetooth modules), he wrote firmware that the Garmin Fenix recognizes as native data. The watch displays the metrics—showing ground contact time and vertical oscillation—just as if the user were wearing an official Garmin HRM-Pro strap.
"Both times the pattern was the same: I knew exactly what I wanted and had no idea where to look," Dumont wrote in a detailed Reddit post documenting his journey.
The technical challenge was immense. Not only did he have to reverse engineer the Bluetooth data stream, but he also had to solve a "two-devices-in-one" problem. The watch wanted the sensor to act as a heart rate monitor, while a separate Connect IQ app needed to read the custom running metrics. Garmin’s BLE stack usually refuses to let one peripheral do both. Dumont’s solution? He made his single chip present as two different devices at two different MAC addresses, switching identities mid-connection without dropping the link. Both blog entries explaining this process are absolutely worth reading for the technical deep-dive.
The AI 'Sparring Partner'
While the hardware hack is impressive, the development process is just as intriguing. Dumont admits he lacks expertise in Bluetooth Low Energy (BLE) and reverse engineering. To bridge that gap, he turned to Claude, an AI coding assistant.
"Claude was very good at two things here," Dumont explains. "It knew how to search for things I didn’t know existed and aggregate the results, and it did the grind."
Claude suggested the "two identities" solution, helped him set up a Bluetooth sniffer to reverse-engineer the real Garmin strap, and wrote most of the firmware state machine. However, Dumont is quick to point out the AI’s limitations. "It was bad at direction," he notes. "It went along with wrong assumptions of mine... and stayed just as confident when it was wrong as when it was right."
The takeaway? According to Dumont, a basic understanding of technology is still necessary. Claude acted as a "technically savvy colleague" and a "rubber duck," but the human had to validate every step on the bench. "Tests, static data and tons of validation were required to make sure nothing was hallucinated."
What This Means for Makers (and Garmin)
Of course, it remains unclear to what extent this project will be adopted by other developers. This is not a plug-and-play solution for the average user, but rather a proof of concept. In the long run, the project—which Sam Dumont also shared via GitHub—could certainly open up opportunities for other makers.
For small hardware startups, this opens a door. They can now build sensors that integrate deeply with the Garmin ecosystem without paying licensing fees or being locked out. For tinkerers, it turns a Garmin Fenix into a universal receiver for custom biometrics.
As for Garmin? They likely won't change their closed strategy overnight. But Dumont’s work proves that software walls, no matter how well built, are rarely unbreakable. Sometimes, all it takes is a programmer, an AI, and a chip that costs less than a sandwich.
Sources: Reddit (r/ClaudeAI), Dropbars.be (Sam Dumont's blog)