The App Acquires Advanced Motion Data at Just 1% of the Regular Cost

By utilizing synchronized video captured using a pair of smartphones, scientists have developed an open-source motion-capture application. This app collects data on human movements and employs artificial intelligence for swift analysis, making it suitable for clinical applications like rehabilitation, pre-surgery planning, and disease diagnostics. Remarkably, it accomplishes this at a mere 1% of the cost associated with conventional technology.

However, Stanford University researchers, supported by funding from the US National Institutes of Health, introduced OpenCap. This innovative system relies on two precisely calibrated iPhones working in tandem to measure human motion and the intricate musculoskeletal processes that underlie movement.

Moreover, it outpaces traditional technologies in data gathering speed and represents a small fraction of the expense incurred by specialized clinics using elaborate setups of approximately $150,000, which typically involve around eight advanced cameras.

Making Human Movement Analysis Inclusive with OpenCap

Senior author Scott Delp, a professor of bioengineering and mechanical engineering at Stanford, mentioned, “OpenCap makes human movement analysis accessible to all. Our aspiration is to make these formerly inaccessible tools available to a broader audience.”

However, the data obtained from this analysis can offer insights for the treatment of individuals dealing with movement-related concerns, aid healthcare professionals in surgical planning, and assess the effectiveness of different therapies. Furthermore, it holds the potential for use in disease screening, particularly in cases where alterations in gait or balance might not be readily apparent during routine medical examinations.

They conducted trials using OpenCap with 100 participants, capturing videos that were subsequently scrutinized by web-based artificial intelligence to evaluate muscle activation, joint load, and joint movement.

The entire data collection process for all 100 participants was completed in under 10 hours, and the analysis results were returned within 31 hours. Each individual’s data collection took approximately 10 minutes, with processing being automatically initiated within the freely accessible cloud platform for researchers.

Co-first author Scott Uhlrich, the director of research in Stanford’s Human Performance Lab, remarked, “What OpenCap accomplishes in minutes would take a skilled engineer days to collect and analyze in terms of biomechanical data. We managed to gather data from 100 individuals in under 10 hours, a task that would have previously taken us a year to complete.”

Exploring Body Landmarks and Forces with OpenCap

The data examines crucial anatomical points on the body, including the knees, hips, shoulders, and other joints, observing their movement within a three-dimensional space. It then utilizes intricate models based on the principles of physics and biology related to the musculoskeletal system to evaluate the body’s motion and the forces involved. This analysis yields significant information about joint angles and the forces exerted on them.

As Delp explained, this system can even identify the specific muscles that are engaged in the process.

In fact, the researchers anticipate that this type of data collection, combined with deep-learning analysis, represents a groundbreaking development in biomechanics research.

The Quantitative ‘Motion-Genome’ of Human Movement

Delp commented, “We have the human genome, but this is essentially going to be the comprehensive ‘motion-genome’ of human movement, captured in a quantitative manner.“

However, he added, “Our aspiration is that by making human movement analysis more accessible through OpenCap, it will expedite the integration of vital biomechanical metrics into an increasing number of research studies, clinical trials, and medical practices, ultimately enhancing outcomes for patients worldwide.”

In fact, the study has been published in PLOS Computational Biology. For more details, you can watch the video below, in which the Stanford team demonstrates the capabilities of OpenCap.

Read the original article on: New Atlas

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