The 3-D tracking systems used in Tokyo may one day enable digital twins of athletes
The technology on display in Tokyo suggests that the future of elite athletic training lies not merely in gathering data about the human body, but in using that data to create digital replicas of it.
There, an artificial intelligence program uses deep learning to analyze an athlete 's movements and identifies key performance characteristics such as top speed and deceleration.
During NBC 's broadcast of the 100 meter trials in Eugene, Ore., the AI showed how Sha'Carri Richardson hit 24.1 miles per hour at her peak and slowed to 20.0 mph by the time she reached the finish line.
"It 's like having your own personal commentator point things out to you in the race," says Jonathan Lee, director of sports performance technology in the Olympic technology group at Intel.
To train their Olympic AI via machine learning, Lee and his team had to capture as much footage of elite track and field athletes in motion as they could.
Tracking this "Skeleton" enables the program to perform 3-D pose estimation on the athlete 's body as it moves through an event.
The tracking system is limited to the track-and-field events at this year 's games.
Baricelli suggests 3DAT 's Olympic debut may be "a big step for research meeting practice-or better, practice embracing research results."
In 2020 the U.S. Air Force began a six-year project to develop a digital twin of a B-1B Lancer bomber to understand how individual parts decay, and how to slow those processes.
She believes engineers will soon be using data collected from wearable fitness monitors and AI tracking tools to deploy digital twins of individual athletes.