  Data-driven methods have become increasingly more prominent for
musculoskeletal modelling due to their conceptually intuitive simple and fast
implementation. However, the performance of a pre-trained data-driven model
using the data from specific subject(s) may be seriously degraded when
validated using the data from a new subject, hindering the utility of the
personalised musculoskeletal model in clinical applications. This paper
develops an active physics-informed deep transfer learning framework to enhance
the dynamic tracking capability of the musculoskeletal model on the unseen
data. The salient advantages of the proposed framework are twofold: 1) For the
generic model, physics-based domain knowledge is embedded into the loss
function of the data-driven model as soft constraints to penalise/regularise
the data-driven model. 2) For the personalised model, the parameters relating
to the feature extraction will be directly inherited from the generic model,
and only the parameters relating to the subject-specific inference will be
finetuned by jointly minimising the conventional data prediction loss and the
modified physics-based loss. In this paper, we use the synchronous muscle
forces and joint kinematics prediction from surface electromyogram (sEMG) as
the exemplar to illustrate the proposed framework. Moreover, convolutional
neural network (CNN) is employed as the deep neural network to implement the
proposed framework, and the physics law between muscle forces and joint
kinematics is utilised as the soft constraints. Results of comprehensive
experiments on a self-collected dataset from eight healthy subjects indicate
the effectiveness and great generalization of the proposed framework.
