  Musculoskeletal models have been widely used for detailed biomechanical
analysis to characterise various functional impairments given their ability to
estimate movement variables (i.e., muscle forces and joint moment) which cannot
be readily measured in vivo. Physics-based computational neuromusculoskeletal
models can interpret the dynamic interaction between neural drive to muscles,
muscle dynamics, body and joint kinematics and kinetics. Still, such set of
solutions suffers from slowness, especially for the complex models, hindering
the utility in real-time applications. In recent years, data-driven methods has
emerged as a promising alternative due to the benefits in speedy and simple
implementation, but they cannot reflect the underlying neuromechanical
processes. This paper proposes a physics-informed deep learning framework for
musculoskeletal modelling, where physics-based domain knowledge is brought into
the data-driven model as soft constraints to penalise/regularise the
data-driven model. We use the synchronous muscle forces and joint kinematics
prediction from surface electromyogram (sEMG) as the exemplar to illustrate the
proposed framework. Convolutional neural network (CNN) is employed as the deep
neural network to implement the proposed framework. At the same time, the
physics law between muscle forces and joint kinematics is used the soft
constraint. Experimental validations on two groups of data, including one
benchmark dataset and one self-collected dataset from six healthy subjects, are
performed. The experimental results demonstrate the effectiveness and
robustness of the proposed framework.
