Observing how an athlete moves is a common way to assess their performance potential or risk for injury. These assessments use visual observations, which can lead to different conclusions based on the coach/doctor who conducts the assessment or the day/time of the observations. In this study, the investigators assessed 542 athletes, ranging in skill from beginner (youth or recreational) to professional (NFL, NBA, FIFA, MLB players). Each athlete completed seven movements while being filmed by motion capture cameras.
These cameras are like those used to bring life-like movements to Gollum in The Lord of the Rings movies and to players in sports video games. By combining the athletes’ camera data with computer-based artificial intelligence, the investigators were able to classify athletes as elite or novice based on how they moved. For a more detailed outcome beyond this basic classification, the athletes also were scored on a scale from 0% (moves like a novice) to 100% (moves like a professional).
This method is a breakthrough in movement assessment that reduces the need to rely on human observers. This method will improve consistency of classifying athletes in movement evaluations by coaches/doctors. In addition, it may be used for sports training and rehabilitation purposes. The investigators’ next step is to use this method to identify those athletes who are more likely to sustain an injury.
For more information, view the abstract