In This Guide
We analyze tracking apps through a developer's lens:
- The importance of BLE & ANT+ connectivity
- Open Source options for privacy and customization
- Top picks for API access and data export
- Integrating app data with your own IoT projects
For the average cyclist, "tracking" simply means seeing a map of where they went. But for the Maker community, a ride is a massive dataset waiting to be harvested. We want cadence, wattage, heart rate, and slope percentage—and we want that data accessible for our own builds.
While Strava dominates the social sphere, it isn't always the best tool for hardware enthusiasts who need granular telemetry or offline mapping. In this article, we look at the mobile applications that offer the best sensor support and data portability for your next bicycle IoT project.
The Connectivity Standard: BLE vs. ANT+
Before choosing an app, you must understand how it talks to your hardware. If you are building a custom speedometer using an ESP32 or an Arduino, you are likely broadcasting over Bluetooth Low Energy (BLE).
Most modern smartphones support BLE natively, making it the preferred protocol for DIY sensors. ANT+ is efficient but often requires specific hardware dongles on mobile devices. When building your own sensors, stick to standard BLE GATT profiles to ensure they show up in the apps listed below.
Battery Warning
High-frequency tracking (1-second intervals) combined with Bluetooth scanning will drain your phone battery rapidly. For long rides, we recommend building a dedicated hardware logger or carrying a power bank.
The Contenders
Ride with GPS
Best for: Navigation and Reliability.
Ride with GPS offers superior route planning compared to its competitors. For makers, its robust GPX and TCX export options make it easy to pull ride data out and visualize it in Python or MATLAB later.
OpenTracks
Best for: Privacy and Open Source.
This is the Maker's choice. OpenTracks is completely open-source and respects your privacy. It writes data directly to standard file formats without uploading to a cloud server. If you want to study the source code to see how to handle GPS interrupts, this is your gold standard.
The Social Giant: Strava
We can't ignore Strava. While the mobile app is somewhat "walled garden," their API is fantastic for developers. By authenticating your custom hardware or web dashboard against the Strava API, you can push your DIY sensor data directly into their ecosystem, combining your custom metrics with their segment leaderboards.
What Can You Do With The Data?
Once you have captured the data from these apps, the real fun begins. Here are three project ideas for the "Great Meets" community:
- Visualizing Slope: Export your GPX file and use Python (Pandas/Matplotlib) to graph your speed vs. elevation change.
- Heatmaps: Use QGIS to overlay your ride history onto a city map to find your most frequented routes.
- Hardware Replay: Feed your ride data into an Arduino to control a fan speed, simulating the wind resistance of your previous ride while you train indoors.
Pro Tip: GPX vs. TCX
When exporting data, choose TCX (Training Center XML) over GPX if you want to retain fitness data like heart rate and cadence. GPX is primarily for location and elevation.
Building your own tracker?
We have a growing repository of open-source GPS loggers and cycling code. Sign up today to fork our repos and share your ride data visualizations!