Turning rowing data into better technique: a collaboration with RP3 and the University of Twente

How can technology help beginner rowers improve their technique when a coach isn’t always available? That’s the question a student team from the University of Twente is exploring in a project with RP3 and MoveLab.

As part of the Sport Sciences minor, students are developing a feedback system that uses rowing data to help athletes understand their technique and identify where they can improve. The project brings together sports science, engineering and connected fitness technology to explore how data can support learning on the rowing machine.

Helping beginners understand their rowing technique

Learning to row involves more than simply pulling harder. Rowers need to coordinate their legs, body and arms in the right sequence, apply force at the right moment and maintain a consistent rhythm.

For beginners, understanding what to change can be difficult, especially when a coach isn’t available to provide continuous feedback. At the same time, coaches can only work with a limited number of athletes at once.

Therefore, using the RP3 rowing machine the student team is developing a system that translates rowing data into visual feedback. The aim is to help rowers recognize what they are doing during each stroke and understand how they can adjust their movements. Rather than simply presenting performance numbers, the system focuses on making technical information easier to interpret and apply.

Three ways to turn data into feedback

The team is exploring different visual feedback screens, each focusing on a specific element of rowing technique.

1. Finding the right rhythm and sequence

An effective rowing stroke depends on the correct sequence of movements. Rowers need to coordinate the drive and recovery phases while maintaining a consistent rhythm.

One feedback screen combines a metronome with a visual representation of the rower's sequencing. This allows users to compare their movements against a target rhythm and see whether their drive is too fast or too slow. By making timing visible, the system gives beginners a clearer reference point to work towards.

2. Understanding when to apply force

Rowing power comes largely from the legs, which are generally stronger than the arms. Learning to apply force effectively at the beginning of the drive is therefore an important part of developing good technique.

To help users practice this, another screen displays a target box and a ball representing their peak force. Rowers aim to bring the ball into the box, receiving feedback on the timing of their peak force and whether they are applying enough force. This turns an abstract measurement into a practical task, giving rowers a visual target to work towards during their session.

3. Comparing their force curve with the ideal

A third screen focuses on the force curve: a visual representation of how force is applied throughout the stroke.

By displaying the target curve alongside the rower's own performance, the system helps users see where their technique differs from the desired pattern. The feedback can point to different aspects of the stroke, such as accelerating the catch, engaging the legs more effectively, improving body swing or coordinating the arms.

Together, these three approaches explore how complex rowing data can be translated into feedback that is easier to understand and use.

Learning from rowers with different experience levels

An important part of the project is testing the feedback with people who have different levels of rowing experience.

As the student explains, the team itself has limited rowing experience. This makes the project an opportunity not only to develop a technical solution, but also to learn more about how rowing technique works in practice.

The team is observing both beginners, some of whom have only rowed a few times, and more experienced rowers and the difference is revealing. According to the student, more experienced rowers demonstrate greater consistency on the feedback screens and tend to show better technique.

These observations help the team understand how the feedback works for different users and where it may be useful in the learning process.

Testing with a range of participants is important because a feedback system designed for beginners needs to be understandable and useful for people who are still learning the fundamentals, rather than only for athletes who already have an established technique.

Connecting university research with practical technology

For MoveLab, the collaboration illustrates how connected fitness technology can support experimentation and innovation in sports.

The RP3 rowing machine provides the performance data, while the student team is exploring how to use that information to communicate technical feedback to the rower. MoveLab contributes to the collaboration through its experience in connected fitness software and the integration of technology with fitness equipment. This combination creates an opportunity to investigate how data can move beyond performance tracking and become a tool for learning.

It also demonstrates the value of bringing different areas of expertise together. Students can apply their academic knowledge to a practical challenge, while collaboration with industry partners helps connect their work to the technology and equipment used in real training environments.

The project is still an exploration of how visual feedback can help rowers improve. Its findings can contribute to a better understanding of how digital tools may support skill development, particularly when individual coaching is not always available.

Making performance data more useful

Connected fitness equipment can capture detailed information about how people train. The challenge is turning that information into something users can understand and act on.

The University of Twente project with RP3 and MoveLab explores precisely that opportunity: using data not just to measure performance, but to help people understand their movements and work towards better technique.

By combining student research, rowing equipment and software expertise, the collaboration shows how universities and technology companies can work together to explore practical solutions for sport.

For MoveLab, these collaborations are an important way to connect technical expertise with new ideas, support experimentation and explore what connected fitness technology can make possible.

The broader question is one that extends beyond rowing: how can we turn the data generated by fitness equipment into feedback that helps people train, learn and improve?

This blog was created in collaboration with the University of Twente students, who lead and own the project as part of their Sport Sciences minor.