Alliance Research Highlights and Late-Breaking Findings
Ongoing research efforts across the Alliance are pushing the boundaries of our understanding and optimization of human performance. This session highlighted some of those late-breaking findings and provided a preview of research to be explored through the posters and demos.
Samuel Benabou from Stanford University presented his research validating a new wearable technology that doesn’t rely on a person’s heart rate or wrist movements like typical smartwatches but instead uses leg movements. “We found that our leg kinematics device had about 40 percent less error than leading smartwatches on the market.”
Meghan Keating from Boston Children’s Hospital discussed new methods to predict female athlete menstrual status. “It’s difficult to find a specific target when using BMI or percent expected body weight in these female athletes… so we thought relative fat content might be a useful marker to determine adequate energy storage to support normal reproductive function.” Her preliminary research results show that a percent fat measurement based on Dual-Energy X-Ray Absorptiometry (DEXA) might be a good clinical tool to predict menstrual dysfunction in female athletes.
Other Presentations During This Session
- Shivani Guptasarma: Immersive Simulation for Intelligent Prosthetic Arms
- Nicolas Philipp: Changes in Countermovement Jump Force-Time Characteristic in Elite Male Basketball Players: A Season-Long Analyses
- Katie Knaus: Multiscale Computational Modeling of Regional Skeletal Muscle Mechanical and Molecular Response to Acute Exercise
- Damjana Cabarkapa: Relationship Between Internal and External Training Load in Professional Male Volleyball Players
- Anthony Gatti: Estimation of Knee Cartilage Pressures During Cycling
- Grace Privett: Acute Skeletal Muscle Fatigue Reduces Cellular Passive and Active Stiffness
- Malley Gautreaux: Synovial-cartilage Organoids for Pre-clinical Modeling of Knee Trauma
- Aidan Glina: Transcriptional Rhythms of Exercise Revealed in Large-scale Human Datasets Through Algorithmic Comparison
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