On-field player workload exposure and knee injury risk monitoring via deep learning

TL;DR

This study demonstrates that deep learning models, specifically a pre-trained CNN, can accurately estimate 3D knee joint moments from motion capture data, enabling on-field knee injury risk assessment without laboratory force plates.

Contribution

It introduces a novel application of deep learning for real-time, portable knee injury risk monitoring using motion capture data, bypassing traditional laboratory equipment.

Findings

Achieved a mean correlation of 0.8895 with source modeling.

Predictions are accurate during critical stance phases for injury risk.

Feasibility of on-field knee injury assessment using deep learning.

Abstract

In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plates and associated downstream biomechanical modeling, and many researchers have approached the problem of portability by extrapolating models built on linear statistics. An alternative approach would be to capitalize on recent advances in deep learning. In this study, using the pre-trained CaffeNet convolutional neural network (CNN) model, multivariate regression of marker-based motion capture to 3D KJM for three sports-related movement types were compared. The strongest overall mean correlation to source modeling of 0.8895 was achieved over the initial 33 % of stance phase for sidestepping. The accuracy of these mean predictions of the three critical KJM associated with anterior cruciate ligament (ACL) injury demonstrate the feasibility of on-field knee injury assessment using deep learning in lieu of laboratory embedded force plates. This multidisciplinary research approach significantly advances machine representation of real-world physical models with practical application for both community and professional level athletes.