Knee osteoarthritis (OA) affects millions of adults each year, leading to pain, reduced mobility, and major demands on healthcare systems across the globe. Traditionally, diagnosing and tracking OA has depended on subjective clinical assessments or expensive imaging techniques, which can delay care and leave patients in the dark about their condition. MAI Motion is changing this landscape with marker-less digital motion capture —a breakthrough technology that offers a simpler, more objective way to assess knee biomechanics . In this post, we’ll look at how MAI Motion is making OA diagnosis and management more precise, accessible, and patient-friendly.
MAI Motion Technology: A Marker-Less Solution
Motion capture technology has been around for years, but it used to mean attaching physical markers or sensors to the body—a process that was cumbersome, expensive, and limited to specialised labs. MAI Motion transforms this experience by using marker-less motion capture . No more straps or suits—just ordinary cameras (like standard RGB cameras or those with depth capability) combined with state-of-the-art artificial intelligence to track movement.
Here’s how it works: MAI Motion ’s AI analyzes video footage to map your body’s posture and joint positions in real time, creating a three-dimensional view through a process called “human pose estimation.” The system then extracts detailed movement data—joint angles, movement patterns, and how your body’s center of mass shifts as you move.
Thanks to modern machine learning , these calculations are fast, highly accurate, and don’t require manual tweaking. Automated motion analysis like this is now powering new possibilities in rehabilitation programs and computer-aided diagnosis, saving both time and cost compared to traditional approaches. What’s more, MAI Motion has proven its reliability and accuracy not just in research labs, but in real-life clinical environments.
Extracting Meaningful Biomarkers from Movement
So, what does MAI Motion actually measure? At its core, it analyzes “motion-based biomarkers”—specific, objective features of your movement that reveal the health and performance of your knee joint. Think of these biomarkers as the unique “signature” of how your knee moves. Examples include how smoothly you transition between positions or the speed and stability of your movements.
Because raw movement data can be complex, MAI Motion uses advanced statistical methods like Principal Component Analysis (PCA) to simplify it and highlight the key patterns that separate healthy knees from osteoarthritic ones. Using a detailed 3D digital model of your posture, the system uncovers subtle changes that might go unnoticed during a standard exam.
With these data-driven biomarkers, clinicians have access to clear, objective insights. They can more precisely track a patient’s progress, spot early warning signs of deterioration, and tailor treatments to individual needs, moving well beyond checklists or guesswork.
Clinical Studies Prove Effectiveness and Efficiency
Recent clinical studies have demonstrated how effectively MAI Motion identifies knee osteoarthritis . In one study, the system reliably distinguished between osteoarthritic and healthy knees, simply by analyzing how people move.
Another study tackled the sit-to-stand (STS) test, a common way to assess lower limb function in OA patients. Researchers compared the traditional five-repetition (5x STS) protocol to a shortened three-repetition (3x STS) version in adults over 45—the typical age group for OA. Results showed that the shorter test provided almost the same biomechanical information—joint angles, movement consistency, and more—but was easier and less tiring for participants. The studies found no meaningful differences in data quality between the two protocols.
This balance of efficiency and accuracy is a major win for both clinics and patients, allowing for faster, less burdensome assessments without sacrificing the quality of information clinicians need.
Balancing Data Quality with Patient Comfort
High-quality data is crucial for diagnosis and tracking, but so is patient comfort—especially for those living with pain or fatigue. That’s why validating the shorter three-repetition STS protocol was such an important step: it provides dependable biomechanical insights while reducing physical strain.
MAI Motion also stands out compared to other technologies, offering results that reflect natural movement, all without requiring cumbersome equipment. Statistical analysis showed that the shorter protocol was just as reliable, meaning patients don’t have to push themselves to exhaustion for quality results.
In short, MAI Motion achieves a careful balance between data accuracy and patient comfort, making it a practical choice for a wider range of clinical settings—including those serving individuals who might struggle with more demanding movement tests.
Bringing MAI Motion into Everyday Clinical Practice
From a practical perspective, MAI Motion is designed for easy integration into existing healthcare routines. It needs only standard, affordable cameras and user-friendly software, with clinical staff able to learn the system quickly and interpret the results with confidence.
The AI-driven technology automatically adjusts for variables such as lighting conditions or patient attire, ensuring consistently high-quality results in different clinical spaces—or even during home visits.
MAI Motion also enables clinics to move away from expensive MRI scans and complex marker-based set-ups, offering similar or better levels of accuracy in a far more accessible form. It’s versatile too: it can support everything from early diagnosis to regular monitoring and even remote telehealth consultations.
Built-in computer-aided diagnosis tools help clinicians make faster, more informed decisions—ultimately leading to more efficient patient care.
Looking Ahead: The Future of Digital Biomechanics
The future for MAI Motion and marker-less digital biomechanics is bright. Ongoing research is exploring enhancements like adding depth sensors for even greater spatial precision, and delivering real-time feedback to patients during physiotherapy for improved rehab outcomes.
Long-term studies are also underway to track patients over months or years, which could deepen our understanding of OA progression and guide decisions about surgery or treatment timing.
Integrating MAI Motion data with electronic health records could further personalize care and support broader health research. Meanwhile, new AI advances are set to make automated movement analysis even faster and more insightful—bringing digital biomechanics squarely into everyday healthcare.
Conclusion
Marker-less motion capture systems like MAI Motion are redefining how knee osteoarthritis is diagnosed and managed. By delivering clear, objective insights without the need for complex equipment or burdensome tests, this technology is making high-quality OA care more accessible and affordable. For clinicians, researchers, and anyone involved in musculoskeletal health , MAI Motion offers an exciting opportunity to improve patient outcomes and advance the science of rehabilitation .
If you’re ready to see how digital biomechanics can upgrade your practice or research, now is the perfect time to explore what MAI Motion has to offer.
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 Arthritis, 11, 053. 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), 5688. https://doi.org/10.3390/app15105688