  Marker-based Optical Motion Capture (OMC) systems and associated
musculoskeletal (MSK) modelling predictions offer non-invasively obtainable
insights into in vivo joint and muscle loading, aiding clinical
decision-making. However, an OMC system is lab-based, expensive, and requires a
line of sight. Inertial Motion Capture (IMC) systems are widely-used
alternatives, which are portable, user-friendly, and relatively low-cost,
although with lesser accuracy. Irrespective of the choice of motion capture
technique, one needs to use an MSK model to obtain the kinematic and kinetic
outputs, which is a computationally expensive tool increasingly well
approximated by machine learning (ML) methods. Here, we present an ML approach
to map experimentally recorded IMC data to the human upper-extremity MSK model
outputs computed from ('gold standard') OMC input data. Essentially, we aim to
predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We
use OMC and IMC data simultaneously collected for the same subjects to train
different ML architectures that predict OMC-driven MSK outputs from IMC
measurements. In particular, we employed various neural network (NN)
architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent
Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent
Unit) and searched for the best-fit model through an exhaustive search in the
hyperparameters space in both subject-exposed (SE) & subject-naive (SN)
settings. We observed a comparable performance for both FFNN & RNN models,
which have a high degree of agreement (ravg, SE, FFNN = 0.90+/-0.19, ravg, SE,
RNN = 0.89+/-0.17, ravg, SN, FFNN = 0.84+/-0.23, & ravg, SN, RNN = 0.78+/-0.23)
with the desired OMC-driven MSK estimates for held-out test data. Mapping IMC
inputs to OMC-driven MSK outputs using ML models could be instrumental in
transitioning MSK modelling from 'lab to field'.
