The Growing Challenge of Knee Osteoarthritis—and the Promise of New Assessment Tools
Knee osteoarthritis (OA) affects millions of people in the UK, taking a major toll on individuals and our healthcare system. Every year, over 90,000 people need total knee replacement surgery, costing the NHS more than £600 million. These numbers make it clear: we urgently need better, more accessible ways to assess and manage osteoarthritis —ideally long before surgery becomes necessary.
OA develops when the cartilage that cushions our joints slowly breaks down, causing pain, stiffness, and difficulty moving. While it’s most common in older adults, factors like excess weight or certain physical jobs can increase the risk. Spotting OA early and tracking knee function over time are key to helping people stay active and maintain a good quality of life.
Enter MAI Motion —a cutting-edge, AI-powered motion capture system that skips the need for markers or expensive machines like MRI scanners. All it needs is standard video, which can even be recorded on a smartphone, to analyse how someone moves. Using biomechanics (the science of movement), MAI Motion makes it much easier and more affordable for clinicians and therapists to measure real-world knee function. As recent research puts it, this method offers a “cheap and sensitive alternative to traditional motion capture techniques” (Armstrong et al., 2024).
Why the Sit-to-Stand Test Is So Important
One of the simplest—and most revealing—functional tests for knee health is the sit-to-stand (STS) test. This test asks someone to stand up from a chair and sit back down, repeatedly. While it may sound basic, this motion involves several key phases: leaning forward, shifting weight, pushing up, and then balancing once upright. Together, these movements reveal how well the muscles and joints work together—critical information for daily life.
Clinicians and researchers use the STS test to assess things like leg strength, balance, and fall risk, especially in older adults or those recovering from injury. A commonly used version is the five-times sit-to-stand test (5× STS), which strikes a balance between informative data and manageable effort. However, some patients—especially those after knee surgery—struggle to complete all five repetitions, with about 18% unable to finish the test. This limits the test’s usefulness for certain groups who could benefit most from monitoring.
Can Fewer Sit-to-Stand Repetitions Give Us the Same Answers?
This challenge led researchers to explore a key question: Could a shorter sit-to-stand test —just three repetitions (3× STS) instead of five—still reveal what clinicians need to know, without exhausting patients? Using MAI Motion , researchers captured precise movement data such as joint angles, force production, range of motion, variation between reps, and overall performance.
The results were striking: three repetitions provided virtually the same insights as five. Across all measured factors, there were no clinically meaningful differences. In other words, using fewer repetitions didn’t sacrifice any important information about how the knee is functioning. The shorter test means less fatigue for patients, making it far more accessible for those who may struggle with endurance or pain. This simple adjustment opens the door for more people to be assessed regularly, both in clinics and during rehabilitation.
Making Knee Assessments More Accessible and Patient-Friendly
The benefits of MAI Motion don’t stop there. Because it’s markerless and works with basic video, people can now assess their knee function at home with nothing but a mobile phone. Remote assessments mean patients don’t need to travel or make clinic appointments—hugely helpful for those with limited mobility, chronic pain, or who live far from healthcare facilities.
Traditional motion analysis often depends on expensive, complex equipment and markers that have to be attached to the skin—approaches that are impractical outside specialised labs. In contrast, markerless video analysis captures natural movement with none of the hassle. This makes regular, objective check-ins not only possible but easy, giving patients and clinics valuable feedback over the course of rehabilitation . The streamlined three-repetition sit-to-stand protocol also makes testing less tiring and more inclusive, allowing more people to participate and keep track of their progress. As a recent study found, participants reported that the three-rep test was noticeably easier, further supporting its use in real-world healthcare (Wen et al., 2025).
Looking Ahead: Smarter Knee Care with AI and Personalised Rehab
The true potential of MAI Motion is just beginning to unfold. Beyond simply measuring movement, combining its detailed data with machine learning could help identify new biomarkers—features that reveal joint health or disease progression. For example, the way someone moves, their balance, or the force they generate could all become valuable indicators for clinicians and patients.
These insights could help tailor rehabilitation precisely to each person, track how osteoarthritis evolves, and measure individual progress more accurately over time. Future research may expand beyond the sit-to-stand test , including other activities like squats or balance tasks, and adapt assessments for a wider range of people—such as stroke survivors or those with muscle loss.
By working alongside existing healthcare pathways, MAI Motion stands to revolutionise knee care. It promises assessments that are quicker, easier, and more widely available, helping people stay mobile and live life to the fullest. As researchers note, these new digital biomarkers could play a crucial role in tracking treatment success and long-term knee health (Armstrong et al., 2024).
Keywords: knee osteoarthritis ; digital motion capture ; biomechanics ; kinematics; movement; patient monitoring; automated rehabilitation ; human pose estimation.
This story of innovation shows how modest changes—like reducing test repetitions and leveraging AI-powered video analysis—can revolutionise our approach to knee osteoarthritis . By putting patients’ comfort and access first, MAI Motion paves the way for better, long-term knee health for all.
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
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