Revolutionising Osteoarthritis Assessment: How Markerless Motion Capture is Transforming Knee Function Evaluation

Miss Sophie Harris
Miss Sophie Harris
Published at: 2/10/2025

Revolutionising Osteoarthritis Assessment: How Markerless Motion Capture is Transforming Knee Function Evaluation

Osteoarthritis is a widespread joint condition that often leads to knee pain and stiffness, making everyday activities like standing up or taking a walk much more difficult. Traditionally, doctors have relied on simple tests — like the sit-to-stand test — to evaluate how well a patient’s knees function . However, these tests can sometimes be inconsistent. Patients might tire out easily or perform irregularly, which can skew results. That's why the technology developed by MAI Motion is such a breakthrough. Their system uses artificial intelligence and markerless motion capture to gather detailed movement data—without the need for bulky sensors or intrusive markers. This provides a far more accurate and comfortable way to understand how osteoarthritis is affecting someone’s knees.

A Smarter, Simpler Way to Analyse Movement

MAI Motion ’s system revolves around an easy-to-perform test: standing up from a chair three times in a row. It may sound simple, but studies show that this quick version gives just as much meaningful data as longer, more exhausting tests. This is a real advantage for people with osteoarthritis , who often find extended or repeated movements painful and tiring. The technology uses advanced computer vision —essentially, a highly trained digital eye—to track the angles and speed of each joint’s movement with impressive accuracy. By skipping traditional physical markers, the assessment feels much more natural for patients and delivers more consistent, reliable results.

Looking Beyond Movement: Understanding Pain and Adaptation

Assessing osteoarthritis isn’t just about tracking joint movement; it’s also about understanding the pain and discomfort people experience when they move. MAI Motion ’s technology goes deeper by analysing how muscles and nerves coordinate each movement. This is vital because many with osteoarthritis unconsciously change their movements to avoid pain. By recording these natural movements in a non-intrusive way, clinicians get a truer picture of how patients actually function day-to-day. Armed with this precise data, healthcare providers can design rehabilitation plans tailored to each person’s unique abilities, challenges, and pain levels. This leads to more targeted therapy and a smoother path to regaining mobility and confidence.

Benefits for Patients and Healthcare Providers

The advantages of markerless motion capture reach beyond patient comfort. Healthcare professionals benefit, too, since the technology allows for remote assessments. Patients can perform the test at home, while clinicians receive real-time, reliable data to monitor progress and adjust treatments as needed. Because there’s no complicated equipment, assessments are easier to set up anywhere—from small clinics to home environments—making care more accessible. On top of this, research points to markerless methods being cost-effective and highly sensitive alternatives to traditional techniques. In the future, the system could use machine learning to automatically identify key movement patterns associated with osteoarthritis , speeding up diagnosis and enabling earlier intervention. By reducing unnecessary clinic visits and personalising care, this technology could lower healthcare costs and improve outcomes for people struggling with joint problems .

Looking to the Future

MAI Motion ’s markerless motion capture technology represents a major leap forward in evaluating and treating knee osteoarthritis . Its blend of precise movement analysis and real insight into how pain affects daily life overcomes the weaknesses of conventional tests. And, with its easy-to-use, efficient design, the approach not only improves diagnostic accuracy but also leads to more personalised and successful rehabilitation programs. As research continues and technology advances, we can expect artificial intelligence and automated analysis to make musculoskeletal care even smarter and more accessible—offering hope for better outcomes and improved quality of life for the millions living with osteoarthritis .

References

Armstrong, K., Zhang, L., Wen, Y., Willmott, A. P., Lee, P., & Ye, X. (2024). A marker-less human motion analysis system for motion-based biomarker identification and quantification in knee disorders. Frontiers in Digital Health. https://doi.org/10.3389/fdgth.2024.1324511

Kai Armstrong, Yan Wen, Lei Zhang, Xujiong Ye, & Paul Lee. (2022). Novel Clinical Applications of Marker-less Motion Capture as a Low-cost Human Motion Analysis Method in the Detection and Treatment of Knee Osteoarthritis.

Wen, Y., Verma, T., Whitehead, J. P., & Lee, P. (2025). Empirical Validation of a Streamlined Three-Repetition Sit-to-Stand Protocol Using MAI Motion. Applied Sciences, 15(10), 5688. https://doi.org/10.3390/app15105688

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