Skeleton is a winter sliding sport in which athletes lie on a small bobsled and travel, headfirst, down a frozen track at terrifyingly high speeds. A skeleton is also an internal framework of bones supporting the body of a person or animal. A skeleton performing the skeleton is, well, an odd sight.
It’s also one that researchers at the U.K.’s University of Bath have been seeing a lot of during a recent motion capture project that could help skeleton athletes in future Olympics to more accurately track their performance. It may have possible application in other sports as well.
Researchers in the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA), the University of Bath’s motion research center, have developed a computer vision and deep learning-assisted technique that is able to analyze video of athletes performing movements and track movement on the skeletal level. What’s more, it’s able to do this without requiring the kind of marker suits typically used in motion capture.
“We have developed a system that requires only video cameras and no markers,” Steffi Colyer, who led the project, told Digital Trends. “We use computer vision and deep learning methods to automatically extract body configuration and the motion of the skeleton sled in this case, with this information then used to better understand how an athlete generates velocity. Several markerless pose-estimation algorithms do already exist. However, the feasibility and accuracy of these are yet to be fully determined — especially in challenging, real-world environments such as the skeleton push-track. Therefore, a validated, custom-built system like the one we have developed here is currently needed for these types of applications.”
The trouble with markers
The reason for trying to get rid of traditional markers is rooted in the application of this particular technology: Namely, helping athletes. Marker-based technologies require a large number of markers — up to 100 — to be carefully attached to specific points on the body. Using this traditional approach, it’s possible to construct a model of the underlying skeleton (the bones, not the sport) in order to be able to pinpoint exactly how an athlete is moving. This data can then be used by coaches to fine-tune athletes’ behavior, letting them know exactly when the person has put a foot wrong. The problem is that covering athletes in dozens of tiny markers isn’t necessarily all that comfortable for the wearer, while also taking a lot of time.
The system developed by the University of Bath researchers has shown itself to be every bit as effective as, but way less compromised than traditional markers. In a test involving 12 athletes doing 33 push trials (the all-important start phase of a skeleton race), both approaches showed almost identical performance in measuring both sled and athlete velocity. The test was carried out at the university’s push-track training facility, a concrete slope kitted out with straight metal rails that’s designed to allow athletes to train during the offseason with a wheeled practice sled.
“Our system can be used to assess and regularly monitor skeleton athletes’ development across the push-start phase,” Colyer explained. “It can be used by coaches to detect strengths and weaknesses of athletes’ performances, which can inform their training — and hopefully improve their chances of success at the Beijing 2022 Winter Olympic Games and beyond.”
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