Adarsh Iyer

gAIt: Deep Learning Based Evaluation of Injurious RunningBiomechanics Using 2-Dimensional Pose Estimation

Started Dec. 9, 2022

Abstract or project description

Biomechanical errors in running form have increasingly been linked to the development of musculoskeletal injuries, precluding runners from the physical and mental benefits of long-distance running. Existing works on biomechanical analysis of running form utilize expensive equipment and focus on understanding injury causes instead of predicting injury risk. In this paper, we present a novel dataset and three deep learning models for video analysis of running biomechanics using 2-dimensional pose estimation. Our approach predicts scores corresponding to the severity of injurious biomechanical errors, enabling runners to improve their form based on these predictions. We collect training examples through web-scraping and supplemental recordings, to which we apply BlazePose, a pre-trained pose estimation model, to obtain spatiotemporal data series. We explore three deep learning models for time series analysis, of which a Residual Neural Network (which we term gAIt) performs best, and all models achieve greater accuracy than baseline K-Nearest Neighbors with Dynamic Time Warping. Our gAIt model and future work with this dataset may provide recreational runners with an accessible and accurate tool to improve their running form in order to stay injury-free.