Big Data Techniques Help Identify Best Strategies for Improving Sport-Relevant Skills
Published May 2020 | Medicine & Science in Sports & Exercise
A unique analysis of biomechanical data sheds light on the connection between organized coordination patterns involved in sport-relevant movements, the anatomical structures that support them, and the task-relevant factors that influence and constrain an athlete’s ability to perform.
Based on 15 years of 3D motion analysis of whole-body movement data collected from adolescent athletes performing the drop vertical jump (DVJ), researchers in the Division of Sports Medicine found that only a few successful DVJ coordination strategies—each with their own distinct performance outcomes—are possible. Lead researcher Christopher DiCesare, PhD, and Gregory Myer, PhD, director of research, say the findings broaden the understanding of movement patterns and risks.
“This approach can simplify the screening process and allow for easier interpretation of the underlying mechanisms that contribute to injury risk,” DiCesare says. “If we know that better strategies exist for athletes than are observed when we screen them, we can develop biofeedback and neuromuscular training strategies that will allow them to optimize sport performance while simultaneously reducing injury risk.”
Data were aggregated from previous studies of 780 adolescent athletes, most of them females. Drop, land and jump tasks involved three specific movement profiles; a similar analysis of the single leg drop landing produced its own distinct, task-specific profiles.
The biggest challenge was controlling for the vast amount of “noisy” biomechanical data due to the inherent variability in human movement, according to Myer.
“Large-scale data science has not been as prevalent with the biomechanical/human movement science community,” he says. “It was challenging to apply these techniques and interpret the results without points of reference in the current literature. The advanced analytic approach employed by Dr. DiCesare can help us identify and treat undesirable movement patterns in the future.”
