Robotic devices hold promise for aiding patients in orthopedic rehabilitation. However, current robotic-assisted physiotherapy methods struggle including biomechanical metrics in their control algorithms, crucial for safe and effective therapy. This paper introduces BATON, a Biomechanics-Aware Trajectory Optimization approach to robotic Navigation of human musculoskeletal loads. The method integrates a high-fidelity musculoskeletal model of the human shoulder into real-time control of robot-patient interaction during rotator cuff tendon rehabilitation. We extract skeletal dynamics and tendon loading information from an OpenSim shoulder model to solve an optimal control problem, generating strain-minimizing trajectories. Trajectories were realized on a healthy subject by an impedance-controlled robot while estimating the state of the subject's shoulder. Target poses were prescribed to design personalized rehabilitation across a wide range of shoulder motion avoiding high-strain areas. BATON was designed with real-time capabilities, enabling continuous trajectory replanning to address unforeseen variations in tendon strain, such as those from changing muscle activation of the subject.

This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial wall. We first proposed a U-Net based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches, respectively, to further improve the prediction accuracy of field maps. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.