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Stanford University’s human performance lab, located adjacent to its physical therapy clinic, often receives requests from orthopedic surgeons for biomechanical analyses of their patients, particularly athletes with recurring injuries.
Previously, this analysis would take days to complete, limiting the number of times it could be performed in a year, explained Scott Uhlrich, PhD, the lab’s research director. However, a newly developed app now accomplishes this task in less than 10 minutes.
The motion-capture application, named OpenCap and devised by Uhlrich and fellow Stanford bioengineers, has the potential to assist clinicians in devising more effective interventions to prevent mobility issues and hasten recovery. Additionally, it can aid researchers in addressing substantial knowledge gaps regarding human mobility.
OpenCap employs smartphone videos, artificial intelligence (AI), and computational biomechanical modeling to quantify movement. Currently, it is accessible for free in research and educational contexts. Model Health, a startup affiliated with the Stanford researchers, offers licenses for commercial and clinical use.
Here’s the process: Footage of human movement, captured by two smartphones, is uploaded to the cloud, where an algorithm identifies specific points on the body. The app utilizes computer vision algorithms, a type of AI that trains computers to interpret visual data — in this case, a person’s posture.
Next, the app measures how the body is moving in three-dimensional space. Models of the musculoskeletal system provide insights into this movement, including joint angles, tendon stretch, and force distribution through the joints — all crucial factors related to injuries and diseases, according to Uhlrich, co-author of the app’s introduction.
The traditional approach to conducting this type of analysis requires specialized expertise and costs $150,000. In contrast, the app is both free and user-friendly.
Senior study author Scott Delp, PhD, a professor of bioengineering and mechanical engineering at Stanford, believes this app “democratizes” human movement analysis and hopes it will lead to improved outcomes for patients worldwide.
A significant amount about human mobility remains unknown. For instance, in aging adults, it’s unclear when balance starts to decline and by how much each year. The causes of sports injuries and the progression of degenerative joint diseases like arthritis are also still being unraveled.
OpenCap could play a substantial role in changing this. Due to its lower cost and ease of use, the app could facilitate much larger studies compared to traditional methods. In one study, the app collected movement data from 100 participants in less than 10 hours and computed results in 31 hours, a task that would have taken a year otherwise.
About 2600 researchers worldwide are already utilizing the app, according to Uhlrich. Many of them had never before created a dynamic simulation.
Eni Halilaj, PhD, an assistant professor of mechanical engineering at Carnegie Mellon not involved in the app’s creation, expressed excitement about the endless opportunities it presents, particularly for conditions that have been challenging to characterize through traditional studies with limited participants.
One example highlights how the app was used to study hamstring strain injuries during sprinting, revealing that these muscles lengthen more rapidly during acceleration compared to running at a constant speed. This information could shed light on the higher injury rates observed in accelerating athletes.
The researchers are actively employing the app to develop new tools, including metrics to identify the risk of anterior cruciate ligament injury in young athletes and to measure balance.
In the future, this technology could enhance annual physical exams by establishing movement as a biomarker. By having patients perform a few movements like walking or standing up, clinicians could assess their disease risk, progression, or risk of falling.
For instance, excessive loading in the knee joint increases the risk of developing osteoarthritis, but this information is not easily accessible to clinicians. Typically, the disease is diagnosed after symptoms appear, even though intervention could occur much earlier. “Prevention is still not as embraced as it should be,” noted Pamela Toto, PhD, professor of occupational therapy at the University of Pittsburgh, who was not involved in the app’s development. “If we could tie the technology to intervention down the road, that could be valuable.”
