  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.
