EMG-informed neuromusculoskeletal models accurately predict knee loading
measured using instrumented implants
We investigated three different methods for simulating
neuromusculoskeletal (NMS) control to generate estimates of knee joint
loading which were compared to in-vivo measured loads. The major
contributions of this work to the literature are in generalizing
EMG-informed and probabilistic methods for modelling NMS control.
A single calibration function for EMG-informed NMS modelling was
identified which accurately estimated knee loads for multiple people
across multiple trials. Using a stochastic approach to NMS modelling, we
investigated the range of possible solutions for knee joint loading
during walking, showing the method’s generalizability and capability to
generate solutions which encompassed the measured knee loads. Through
this stochastic approach, we were able to show that a single degree of
freedom planar knee is suited to estimating total knee loading, but is
insufficient for estimating the directional components of load.