The Department of Human Movement Sciences (UMCG Groningen) is using Xsens to push anterior cruciate ligament (ACL) prevention research into more realistic football settings. From validating sport-specific movement data to testing interventions for implicit learning, the research shows how wearable motion capture can bring injury prevention research closer to the game itself.
Researchers in the Department of Human Movement Sciences wanted to study how female football players can better learn movements to reduce the risk of ACL injuries in real-world settings, without compromising biomechanical data quality.
The team used Xsens to capture full-body motion during sport-specific football tasks, creating a strong foundation for field-based biomechanics and motor learning research.
Movement patterns improved over time: The intervention helped female football players adapt high-risk football movements linked to ACL injury.
The richest feedback approach worked best: Players improved most when video instruction, self-video, and verbal feedback were combined.
Xsens enabled on-pitch biomechanics: The team could capture full-body movement outside the lab while maintaining lab-grade precision.
ACL injuries can sideline young football players for months. For female players, the risk is even higher. At the Department of Human Movement Sciences (UMCG), researchers aim to understand how these injuries occur during real football actions and how athletes can learn safer ways to move before injury occurs.
The researcher Eline Nijmeijer, together with primary investigator Anne Benjaminse, took on investigating high-risk actions, including landing, deceleration, and changing direction, for her PhD thesis "Learning to move, moving to improve: Applying motor learning principles to reduce ACL injury risk". To capture those movements in a way that reflects real training conditions, the team combined several tools for biomechanical analysis and motor learning, including Xsens motion capture.
ACL injuries happen during fast, reactive, sport-specific moments. That is why the research focused on football movements such as sidestep cutting and jump landings, which are closely linked to ACL injury mechanisms.
The goal was not only to measure how young athletes move, but also to test how movement patterns can be improved over time. Female football players followed a 4-week training program, and researchers compared baseline and retention measurements to track lasting change.
For this project, the team needed motion capture that could move with the research. Xsens helped capture full-body movement not only in controlled testing but also on the football field, with lab-grade precision.
That portability was essential.
“We wanted to measure the players on the field, and we knew that optical motion capture was impossible, but with Xsens it was,” said Eline Nijmeijer.
The suit-based setup also played an important role. For dynamic football movements, the researchers preferred the full-body suit because it stayed in place better and supported reliable full-body measurement across repeated sessions.
Before collecting the field data, the team first validated Xsens with an optical motion capture reference in the lab-based part of the research. That gave them confidence that the data collected outside the lab could be interpreted with credibility.
The validation study showed excellent agreement for sagittal plane lower body kinematics across all joints and tasks, with cross-correlation values above 0.92. Hip, knee, and ankle flexion and extension waveforms aligned especially well during sport-specific actions such as jump landings, decelerations, and sidestep cuts. Hip measurements in the frontal plane also showed strong agreement. At the same time, the study found more variability in frontal and transverse plane measurements at the knee and ankle, meaning those angles required more caution in interpretation.
The key idea behind the research was simple. Athletes do not always learn best from a long list of verbal instructions. Instead of overloading players with detailed cues about knees, ankles, and hips, the researchers used video-based learning.
To test the hypothesis, the players were split into four groups:
A group that received verbal feedback from the test leader, in addition to the instruction and playback videos.
That combination mattered. The group that received the richest mix of instruction and feedback showed the greatest improvements over time, especially in hip- and knee-abduction-related variables.
The broader takeaway is that implicit motor learning principles acquired through observation can help athletes improve movement patterns during sport-specific tasks. In practice, that opens the door to more effective injury prevention strategies that fit real coaching environments.
With Xsens, the Department of Human Movement Sciences is helping push ACL research into a more practical future. By combining biomechanics, motor learning, and wearable motion capture, the team is building a clearer picture of how female football players move, how those patterns can be improved, and how injury-prevention research can get closer to the game itself.
Read the full thesis: https://research.rug.nl/en/publications/learning-to-move-moving-to-improve-applying-motor-learning-princi/
Learn more about Xsens for biomechanical research in sports.