by Researchers from Stanford University have developed an open-source motion-capture app called OpenCap that utilizes synchronized video from two iPhones to gather human movement data. The app, which was created with funding from the US National Institutes of Health, can be used for clinical purposes such as rehabilitation, pre-surgery planning, and disease diagnostics. OpenCap offers a much more cost-effective solution compared to traditional technology, coming in at just 1% of the cost of specialized clinics’ setups that typically rely on high-tech cameras and can cost around $150,000.
OpenCap democratizes movement analysis by making the once costly technology more accessible, says senior author Scott Delp, a professor of bioengineering and mechanical engineering at Stanford. The app provides valuable data that can inform treatment for patients with movement issues, help clinicians plan for surgeries, assess the effectiveness of different treatments, and even potentially be used to screen for diseases that can affect gait or balance.
During testing, the researchers recorded video of 100 participants using OpenCap and analyzed the data using an artificial intelligence system. The analysis involved assessing muscle activation, joint load, and joint movement. The entire data collection process for all participants took less than 10 hours, and the analysis results were returned in just 31 hours. Each individual data collection took approximately 10 minutes, with processing automatically initiated in the cloud platform.
Co-first author Scott Uhlrich, director of research in Stanford’s Human Performance Lab, highlighted the significant time savings provided by OpenCap. He mentioned that it would typically take an expert engineer days to collect and process the same biomechanical data that OpenCap provides in minutes. Uhlrich explained that they were able to collect data from 100 individuals in under 10 hours, which would have taken a year using traditional methods.
OpenCap studies the movement of body joints in three-dimensional space and uses physics and biology models to assess how the body is moving and the forces involved. It can even identify the specific muscles being activated during movements. The researchers believe that this kind of data gathering and analysis, combined with deep learning, will mark a new era for biomechanics research.
The research team hopes that OpenCap will accelerate the incorporation of important biomechanical metrics into various studies, trials, and clinical practices, ultimately improving patient outcomes worldwide. The study documenting the development and potential applications of OpenCap was published in PLOS Computational Biology.
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1. Source: Coherent Market Insights, Public sources, Desk research
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