Arduin-Row is a maker-built rowing feedback prototype by Justin Lutz. It uses motion sensing and a small machine-learning model to classify three rowing-tempo states and flag unusual handle movement, then sends prompts to an Arduino IoT dashboard. Its reported results describe one build and its training data—not a validated commercial coach or a proven way to prevent injury.
What Arduin-Row does
The prototype mounts an Arduino Nicla Sense ME as a shield on an Arduino MKR WiFi 1010. The Nicla supplies accelerometer readings and onboard environmental sensing; the MKR provides Wi-Fi connectivity for the dashboard. Firmware processes motion data with a deployed Edge Impulse classifier and sends text feedback through Arduino IoT Cloud/Remote.
The classifier labels motion as “easy,” “low-spm,” or “hi-spm.” An anomaly-detection result can append a reminder about handle movement. Prompts shown in Lutz’s project instructions include “Push with your legs!”, “Good power at low strokes per minute!”, “Keep the pace up. High strokes per minute!” and “Keep the handle level!” These are outputs from this prototype, not coaching guidance independently validated for other rowers or machines.
The dashboard also plots the Nicla’s estimated eCO2 reading. That visualization is an additional project feature; it is not evidence that the device is a validated indoor-air safety monitor.
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How the build and model were put together
Lutz describes collecting about 18 minutes of data across the three tempo classes—easy, low strokes per minute, and high strokes per minute—and using those samples to train the model. Hackster.io News also reports the three states and approximately 18 minutes of data. That is the dataset used for this build, not a recommended minimum for a reliable rowing classifier.
Arduino’s July 2, 2022 feature says the trained model recognized the current motion about 98% of the time. This is the project’s reported result, not an independent benchmark, a study across a broad rowing population, or a guarantee of performance on a different user, machine, mounting position, or dataset. The classifier’s usefulness depends on how well its training examples represent the conditions in which it is used.
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The project frames technique feedback around leg drive followed by the arm pull, and uses handle trajectory as one cue. It is intended to explore how a sensor and model might make movement easier to observe; its prompts should not be treated as medical or safety advice.
Why the project uses two Arduino boards
Lutz initially planned to run the Nicla on its own and connect it to a BLE app. He reports running into memory problems with a roughly 20 kB model on the board, which he attributed to 64 kB of RAM and additional packages loaded at runtime. He changed the design to use the Nicla as a shield on the MKR WiFi 1010, which provides the Wi-Fi link to the cloud dashboard.
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In that shield configuration, Lutz found the accelerometer rate topped out at 10 Hz, so he downsampled data that had originally been collected at 100 Hz. He also found consistent board orientation important. These are observations about his hardware and software configuration, not universal specifications for every board revision, firmware, or library combination.
What reproducing the build involves
Lutz’s parts list names one Nicla Sense ME, one MKR WiFi 1010, and a 3.7 V battery. The described setup involves soldering the shield configuration and powering the boards by USB or a JST-connected LiPo battery. Before buying components or wiring a recreation, check current board documentation, connector compatibility, library versions, and the exact wiring instructions in Lutz’s project instructions. The documented build’s use of the Nicla as a shield means the Nicla alone does not recreate its Wi-Fi dashboard setup.
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- Assemble the documented hardware: use the Nicla Sense ME, MKR WiFi 1010, and the specified power arrangement. Follow the project’s soldering and wiring details, checking them against the documentation for the exact boards you have.
- Prepare the motion data and model: the project uses Edge Impulse tooling to build and deploy a classifier for its three recorded tempo states, along with anomaly detection for handle movement. Its roughly 18 minutes of training data describes Lutz’s build; it does not establish how much data another rower needs.
- Configure the dashboard connection: the firmware sends prompts through Arduino IoT Cloud/Remote over the MKR’s Wi-Fi connection. Confirm that current software versions and service configuration work with your chosen board setup.
- Keep the physical setup consistent: mount and orient the sensor consistently with the training setup. A change in placement, orientation, machine, or rowing style can change the readings the model receives.
What to consider before relying on its feedback
- It is a prototype, not an off-the-shelf rowing coach. The published project documents a maker build rather than a product validated for general coaching use.
- Its labels are narrow. The model distinguishes three tempo states and uses anomaly detection for handle movement; it does not establish a comprehensive assessment of rowing technique.
- Its performance is setup-dependent. The reported approximately 98% recognition result belongs to the project report and its own training and test conditions, not to every rower or environment.
- Reproduction may require adaptation. The author’s memory and sampling-rate observations arose from his configuration, and current software or hardware behavior is not established by the 2022 project coverage.
- Availability and cost are uncertain here. The project sources do not establish current hardware stock, pricing, or whether the original software workflow reproduces unchanged today.
Sources and project record
The primary build instructions are Justin Lutz’s Hackster.io project, published July 11, 2022: Arduin-Row! Your TinyML rowing machine coach!. Arduino’s July 2, 2022 feature reports the approximately 98% motion-recognition result: Arduin-Row uses tinyML to improve your rowing technique. Edge Impulse’s project list categorizes Arduin-Row as an accelerometer/activity project using the Nicla Sense ME: Edge Impulse project list. Hackster.io News provides a secondary overview of the three classes, approximate data-collection time, and feedback concept: February’s Impact Spotlights Showcases Innovative Fitness-Based Project Builds.
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