– Garmin’s ecosystem is not fully open, restricting third-party sensor pairing for metrics like running efficiency.
– A developer successfully sent ground contact time and vertical oscillation data to a Garmin Fenix via ESP32/nRF52832, recognized as native data.
– The project relied on Claude AI as a coding assistant, highlighting AI’s value for tasks requiring specialized knowledge (e.g., Bluetooth Low Energy).
– A basic understanding of technology remains necessary despite AI assistance.
– The open-source GitHub project could enable future DIY sensor development within Garmin’s platform.
Garmin’s Ecosystem and Its Limits
Garmin provides a comprehensive ecosystem but it is not entirely open, 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 metrics such as running efficiency. This might be less relevant for end users, but potentially significant for makers who want to experiment with custom hardware.
DIY Sensor Success With ESP32
A programmer has now successfully sent data to a Garmin Fenix using an ESP32 or nRF52832 chip, which the Fenix smartwatch recognised as native data. Specifically, these are running efficiency metrics, namely ground contact time and vertical oscillation. Sample data was used for this, not actual data collected by a fully functional DIY sensor, but it still shows real promise.
Development Process and Claude’s Role
Both the detailed Reddit post and the two blog entries are definitely worth reading, as they also explain how the development process unfolded. Sam Dumont used Claude as a tool and, by his own account, needed this assistance because he lacks expertise in Bluetooth Low Energy and reverse engineering, though he has been familiar with the Garmin platform and its quirks since 2020. The post demonstrates how Claude can apparently be put to good use in programming as a technically savvy colleague who critically examines one’s own ideas and may offer new approaches. According to Dumont, however, a basic understanding of technology is still necessary for such projects.
Future Potential for Makers
Of course, it remains unclear to what extent this project will be adopted by other developers. In the long run, the project, which Sam Dumont also shared via GitHub, could certainly open up opportunities for other makers to create their own sensors that work with Garmin devices.
- Garmin ecosystem is not fully open for third-party sensors
- ESP32 and nRF52832 chips used to mimic native data
- Metrics include ground contact time and vertical oscillation
- Development relied on Claude AI for Bluetooth expertise
- GitHub project could enable future maker innovations