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Musculoskeletal models have been widely used for detailed biomechanicalanalysis to characterise various functional impairments given their ability toestimate movement variables (i.e., muscle forces and joint moment) which cannotbe readily measured in vivo. Physics-based computational neuromusculoskeletalmodels can interpret the dynamic interaction between neural drive to muscles,muscle dynamics, body and joint kinematics and kinetics. Still, such set ofsolutions suffers from slowness, especially for the complex models, hinderingthe utility in real-time applications. In recent years, data-driven methods hasemerged as a promising alternative due to the benefits in speedy and simpleimplementation, but they cannot reflect the underlying neuromechanicalprocesses. This paper proposes a physics-informed deep learning framework formusculoskeletal modelling, where physics-based domain knowledge is brought intothe data-driven model as soft constraints to penalise/regularise thedata-driven model. We use the synchronous muscle forces and joint kinematicsprediction from surface electromyogram (sEMG) as the exemplar to illustrate theproposed framework. Convolutional neural network (CNN) is employed as the deepneural network to implement the proposed framework. At the same time, thephysics law between muscle forces and joint kinematics is used the softconstraint. Experimental validations on two groups of data, including onebenchmark dataset and one self-collected dataset from six healthy subjects, areperformed. The experimental results demonstrate the effectiveness androbustness of the proposed framework.

Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG