"Keep Me In, Coach!": A Computer Vision Perspective On Assessing Acl Injury Risk In Female Athletes

We present and share(1) a foundational dataset of multi-angle video recordings of scripted athletic movements to enable the development of computer vision research applications that evaluate and identify lower-body injury risk. The focus of the dataset is female athletes, who are at a substantially increased risk of anterior cruciate ligament (ACL) injury and are therefore a top priority for sports science. In our study, varsity and club sport athletes perform two assessment movements (the countermovement jump and the drop jump). These jump tasks are used ubiquitously in sports medicine research to characterize athleticism and to identify risk factors that indicate ACL injury propensity. The novelty of the dataset centers on (i) the type of movement data (purposeful, evaluative movements that need to be tracked with a high degree of precision), (ii) our generalized collection method that can be replicated with ease by non-experts, and (iii) the amount of data collected (we collected data from 55 division one (D1) female athletes performing 3 - 5 iterations of each jumps, for a total of 480 jumps). Data from each camera was manually aligned and a fully automated pipeline was built to extract knee information from athletes. Ideally, any athlete or researcher will be able to easily replicate our setup and assemble a compatible and complementary dataset to propel the development and assessment of injury propensity models.