Chronic musculoskeletal conditions like arthritis and joint pain are becoming more widespread worldwide, adding significant pressure on healthcare systems. This growing challenge makes effective preventive care more crucial than ever. Luckily, innovative technologies are now available to help spot problems early—before they become serious. One such breakthrough is MAI Motion : an AI-powered, markerless motion-capture system that offers fast, accessible assessments of movement and joint function.
In this article, we’ll look at how MAI Motion works, how it detects early signs of musculoskeletal problems through biomechanical “biomarkers,” its integration into telehealth , and the real-world benefits it brings—such as preventing knee osteoarthritis . We’ll also discuss future developments that could make preventive healthcare even more accessible and effective.
What Is MAI Motion Technology?
MAI Motion is a digital platform that captures detailed movement information using nothing more than a regular video camera—no special equipment, sensors, or markers required. By recording your movement on a phone or camera, then applying advanced artificial intelligence , MAI Motion analyzes the footage and builds a three-dimensional model of your posture and joint angles.
The heart of the system is deep learning—a form of AI that accurately detects the positions of limbs and joints from ordinary video. Once it has this data, MAI Motion uses principal component analysis (PCA), a statistical method that distills complex movement patterns into the most important features related to joint function.
What does this mean for you or your patients? Imagine filming a short video of someone standing from a chair, and within minutes getting a detailed report of their joint movements—no specialized lab, no complicated setup. MAI Motion turns a simple video into a powerful, low-cost tool for professionals to assess mobility and detect potential issues early on. Research has validated the system’s ability to capture clinically relevant motion data using just standard cameras.
Spotting Early Biomechanical Biomarkers
Our movements can reveal much about our joint health . Specific patterns in the way we move—known as biomechanical biomarkers—can signal underlying problems long before pain or injury arises.
MAI Motion excels at identifying these subtle signs. For example, it can analyze how smoothly your knees bend as you stand up, or measure the overall acceleration of your movements. By applying PCA, it extracts the small but telling details hiding in the data.
Traditional methods may overlook these early warning signs, but MAI Motion provides a more sensitive and cost-effective approach. By recognizing issues sooner, healthcare professionals can intervene earlier—helping to prevent chronic conditions or stop them from worsening.
Bringing MAI Motion into Telehealth
As telehealth becomes part of everyday healthcare, the need for easy-to-use remote assessment tools is greater than ever. MAI Motion fits perfectly into this landscape, offering a user-friendly mobile app that guides people through a short, three-repetition sit-to-stand test using just their phone’s camera.
Importantly, new research has shown that using three repetitions—rather than the traditional five—offers similar reliability and accuracy. Key indicators, such as the cumulative acceleration and smoothness of knee and elbow movements, can be measured accurately using this streamlined approach. This means MAI Motion balances convenience with comprehensive, clinically relevant results—without burdening users or requiring clinic visits.
The simplicity and speed of the test make it accessible, while the quality of data remains on par with longer, more complex lab-based assessments. And, because there’s no expensive equipment involved, the costs are dramatically reduced—making remote health monitoring open to far more people.
By connecting patients and clinicians across distances, MAI Motion supports ongoing care and encourages proactive health management—both essential for successful preventive healthcare. It can also help track the success of treatments, rehabilitation progress, or disease progression over time.
A Real-Life Example: Preventing Knee Osteoarthritis
Consider someone in midlife who starts to feel mild knee discomfort but hasn’t yet seen a doctor. With MAI Motion , they can do a quick, at-home sit-to-stand test using their phone. The app analyzes their movement and joint angles, providing results as reliable as those from the longer five-repetition tests used in clinics.
With these early insights, a healthcare provider can quickly suggest a personalized exercise plan to improve joint health —potentially slowing or even halting the progression of knee osteoarthritis , a common and often disabling condition.
This example illustrates how easy, remote assessments with MAI Motion can catch problems early and enable tailored care, leading to better long-term outcomes.
What’s Next for MAI Motion?
Looking ahead, MAI Motion is expected to become even more powerful. Future plans include expanding the range of biomechanical biomarkers it can detect and more seamlessly integrating results with electronic health records, making it easier for clinicians to access and use the information in daily practice.
Ongoing advancements in AI will further improve accuracy across diverse populations and a wider variety of movements. Predictive models may one day help identify those at risk before symptoms even start.
Taken together, these innovations promise to make preventive healthcare smarter, more efficient, and more widely accessible—helping to reduce the burden of chronic musculoskeletal conditions everywhere.
In summary, MAI Motion leverages advanced AI and everyday video devices to provide in-depth movement assessments , all without the need for expensive hardware or in-person clinic visits. By detecting early biomechanical biomarkers and fitting seamlessly into telehealth , it brings accessible and powerful tools to preventive healthcare.
Clinicians, researchers, and healthcare leaders should consider bringing MAI Motion into routine care. By embracing these innovations, we can help everyone enjoy healthier joints and better mobility in the years ahead.
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. . https://doi.org/10.3389/fdgth.2024.1324511
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. . https://doi.org/10.4172/2167-7921.2022.11.053
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