  Monitoring athlete internal workload exposure, including prevention of
catastrophic non-contact knee injuries, relies on the existence of a custom
early-warning detection system. This system must be able to estimate accurate,
reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near
real-time and during match play. However, current methods are constrained to
laboratory instrumentation, are labor and cost intensive, and require highly
trained specialist knowledge, thereby limiting their ecological validity and
wider deployment. An informative next step towards this goal would be a new
method to obtain ground kinetics in the field. Here we show that kinematic data
obtained from wearable sensor accelerometers, in lieu of embedded force
platforms, can leverage recent supervised learning techniques to predict near
real-time multidimensional ground reaction forces and moments (GRF/M).
Competing convolutional neural network (CNN) deep learning models were trained
using laboratory-derived stance phase GRF/M data and simulated sensor
accelerations for running and sidestepping maneuvers derived from nearly half a
million legacy motion trials. Then, predictions were made from each model
driven by five sensor accelerations recorded during independent
inter-laboratory data capture sessions. The proposed deep learning workbench
achieved correlations to ground truth, by maximum discrete GRF component, of
vertical Fz 0.97, anterior Fy 0.96 (both running), and lateral Fx 0.87
(sidestepping), with the strongest mean recorded across GRF components 0.89,
and for GRM 0.65 (both sidestepping). These best-case correlations indicate the
plausibility of the approach although the range of results was disappointing.
The goal to accurately estimate near real-time on-field GRF/M will be improved
by the lessons learned in this study [truncated].
