Osteoarthritis is one of the most common joint conditions worldwide, causing pain, stiffness, and limited mobility for millions of people. It can turn simple daily tasks into real challenges and places a heavy load on healthcare providers tasked with diagnosing and managing the condition. Traditionally, assessing mobility and joint function requires in-person appointments, special equipment, and expert supervision. These hurdles make care difficult, especially for people who have trouble getting around or live far from clinics.
That’s where MAI Motion comes in—a cutting-edge, markerless motion capture technology designed to make mobility assessment for osteoarthritis simpler, faster, and more accessible. By harnessing standard video recordings and advanced artificial intelligence , MAI Motion provides a practical, user-friendly alternative to old-school methods. In recent research, markerless motion capture has been shown to extract detailed biomechanical features from video footage, giving clinicians valuable insights into a patient’s movement. Studies have also found that MAI Motion can gather the same high-quality data with shorter protocols—meaning less hassle for both patients and clinics.
What Do We Mean by Mobility and Tolerance in Osteoarthritis?
When we talk about mobility in the context of osteoarthritis, we’re referring to how far a joint can move—like bending or straightening a knee. Tolerance, on the other hand, measures how much movement a joint can handle before symptoms like pain or fatigue set in. Both are crucial to understanding how osteoarthritis impacts daily life.
To measure these factors, clinicians look at biomechanical data: things like joint angles (how much the joint bends), how much those angles change from one movement to the next, and the consistency of those movements. For example, if someone’s knee bends to a slightly different angle every time they stand up, that inconsistency can suggest instability or discomfort.
These details aren’t just academic—they’re powerful indicators of joint health and can help track how osteoarthritis is progressing or how well a patient is responding to treatment. The key challenge? Collecting this meaningful data without forcing patients to endure long, tiring tests.
How Does MAI Motion’s Markerless Technology Work?
Traditional motion capture methods often involve sticking reflective markers onto a patient’s body and using special cameras in a controlled lab setting. This process can be uncomfortable and limits when and where assessments can take place.
MAI Motion takes a different approach. Using artificial intelligence and computer vision, it can analyse ordinary video footage—captured with something as simple as a smartphone or tablet—without any need for markers. The software automatically tracks joint movements as the person moves naturally, turning basic videos into rich biomechanical data.
This markerless approach is a game changer for accessibility. Clinicians can perform assessments in any clinical setting, or even remotely, letting patients complete important mobility tests from the comfort of home. There’s much less prep time and much less discomfort—especially important for individuals with limited mobility. The ability to capture movement data naturally also means that the results are more representative of how patients really move in everyday life.
The Three-Repetition Sit-to-Stand Test: A Practical Use Case
One standout application of MAI Motion is in analysing the sit-to-stand (STS) test—a simple, widely used way to check lower body strength, balance, and overall function. Traditionally, patients are asked to repeat this movement five times, but recent research has shown that three repetitions can deliver equally reliable results.
Why does this matter? A shorter test means less effort and less fatigue, which increases the chances that patients can complete the assessment and produce accurate results. In fact, participants have reported that the three-rep version is easier and more comfortable. Research shows that there’s almost no difference in the key biomechanical measurements, such as joint angles and movement consistency, between the three- and five-repetition versions.
Thanks to the precision of MAI Motion’s tracking, clinicians can now use the three-repetition sit-to-stand test to monitor progress, adjust treatments, and assess a patient’s functional abilities—all while reducing strain and time commitment for the patient. This opens the door to friendlier, more efficient testing protocols.
Looking Ahead: Remote Care and Personalised Rehabilitation
MAI Motion’s markerless technology lines up perfectly with the growing movement towards digital health and remote care. For people who may struggle to attend in-person appointments, being able to assess mobility at home is a true breakthrough.
Even more exciting, advances in artificial intelligence mean these kinds of tools can eventually offer even deeper insights, helping clinicians personalise rehabilitation plans that adjust as patients recover and improve. This leads to more tailored care, better results, and more efficient use of healthcare resources—a major benefit for both patients and systems like the NHS.
In Summary
Mobility and movement tolerance assessments are essential for managing osteoarthritis, but they shouldn’t be a burden. With MAI Motion’s markerless motion capture, clinicians get accurate, insightful data without the inconvenience and discomfort of traditional testing methods. By turning everyday video footage into actionable insights, MAI Motion makes assessments faster, easier, and accessible to more people than ever.
Streamlined tools like the three-repetition sit-to-stand test mean reliable outcomes with less strain for the patient. As this technology continues to evolve, it promises to bring remote monitoring and personalised rehab within reach, leading to better care and reduced stress on healthcare systems.
With innovations like MAI Motion, the future of osteoarthritis care is looking brighter, more connected, and more patient-centred.
References
Armstrong, K., Wen, Y., Zhang, L., Ye, X., & Lee, P. (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. Journal of Novel Physiotherapies, 11(5). https://doi.org/10.4172/2167-7921.2022.11.053
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), Article 5688. https://doi.org/10.3390/app15105688