  In this research, we present an innovative method known as a physics-informed
neural network (PINN) model to predict multi-joint kinematics using
electromyography (EMG) signals recorded from the muscles surrounding these
joints across various loads. The primary aim is to simultaneously predict both
the shoulder and elbow joint angles while executing elbow flexion-extension
(FE) movements, especially under varying load conditions. The PINN model is
constructed by combining a feed-forward Artificial Neural Network (ANN) with a
joint torque computation model. During the training process, the model utilizes
a custom loss function derived from an inverse dynamics joint torque
musculoskeletal model, along with a mean square angle loss. The training
dataset for the PINN model comprises EMG and time data collected from four
different subjects. To assess the model's performance, we conducted a
comparison between the predicted joint angles and experimental data using a
testing data set. The results demonstrated strong correlations of 58% to 83% in
joint angle prediction. The findings highlight the potential of incorporating
physical principles into the model, not only increasing its versatility but
also enhancing its accuracy. The findings could have significant implications
for the precise estimation of multi-joint kinematics in dynamic scenarios,
particularly concerning the advancement of human-machine interfaces (HMIs) for
exoskeletons and prosthetic control systems.
