Session I: Digital Twins to Optimize Training, Equipment, and Recovery
Thanks to recent advancements in computational modeling, wearable sensing, and device design, researchers can now create personalized insights to improve the effectiveness of training, optimize equipment, and speed up recovery from injuries like never before. In Dr. Karen Liu’s presentation, she explored the current state of digital twins and whether we can reach a ChatGPT level of intelligence.
“If we build something like that, we can probably predict anything from the outcome of muscle training to sport technique improvements to injury prevention,” said Liu, a member of the Alliance’s Digital Athlete moonshot at Stanford University. “Or we can use digital twins to build exoskeletons or create more effective surgical planning. And we can make those digital twins both generalized and personalized to individuals — but only if we have the data.” So do we have the kind of data that we need?
For answers to that question and more, we invite you to visit the session’s recording at our YouTube channel.
Other Presentations During This Session
- Dr. Greg Appelbaum’s research on how visual training in athletes – not just muscle training – can significantly impact performance. Dr. Appelbaum is a member of the UC San Diego Innovation Hub, the Triton Center for Performance and Injury and Science.
- Dr. Karl Zelick’s thoughts on how we can synthesize digital twins with wearable devices like lower-limb exoskeletons. Dr. Zelick is an Alliance Agility Project Awardee, working closely with the Wu Tsai Human Performance Digital Athlete moonshot team.
- Dr. Michael Snyder on how wearable sensors and deep molecular profiling can be used to improve health and performance, such as testing for COVID or Lyme disease. He also provided insight into how people’s glucose levels vary wildly in response to the same foods. Dr. Snyder is co-lead of the Molecular Athlete moonshot at Stanford University.
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