Abstract: An example method for predicting risk of running-related injury, includes: retrieving a first dataset including running-related data; comparing a first distribution of the first dataset to a second distribution of a second dataset including running-related data; based on the comparison, selecting one of the first dataset or the second dataset; scaling a runner profile from the first dataset based on the selected one of the first dataset or the second dataset; inputting, into a trained machine learning model, the runner profile; and predicting, using the trained machine learning model, a risk of a musculoskeletal injury based on the runner profile.

Abstract: Machine learning-based systems and methods for predicting running-related injuries and related training methods are described herein. An example method for training a machine learning model includes receiving a dataset including running-related data, where the running-related data includes a plurality of samples tagged with respective running-related injury labels. The method also includes augmenting the dataset, where the augmented dataset further includes a plurality of synthetic samples tagged with respective running-related injury labels. The method further includes training a machine learning model using the augmented dataset. The trained machine learning model is configured for predicting risk of running-related injury.