Personalised rehabilitation is essential for restoring shoulder function following injury or surgery, particularly due to the joint’s anatomical complexity and variability across patients. While robotic systems offer consistent and intensive therapy, they often neglect internal biomechanical stress, such as tendon strain, potentially resulting in movements that appear safe externally but exceed physiological limits.

Recent approaches have integrated strain maps into robotic rehabilitation, but remain limited to patient-led motion. This presents a major limitation during early-stage rehabilitation, when patients cannot initiate movement themselves. To address this, we present a method that enables robot-led execution of therapist-defined shoulder movements, while adapting these movements to individual patient anatomies using musculoskeletal strain data.

Our system uses Dynamic Movement Primitives (DMPs) to encode a demonstrated shoulder trajectory, then adapts the motion based on strain maps generated in OpenSim. Regions of elevated tendon strain are modelled as ellipsoidal high-strain zones. These zones exert repulsive forces during trajectory execution, and an adaptive time scaling mechanism slows motion near unsafe areas to ensure smoothness and safety.

We validated the system using two input trajectories: one manually drawn and one recorded through kinaesthetic teaching with a KUKA LBR iiwa-7 robot. Both were adapted across six virtual patient models with varying tendon insertion points. In all cases, the adapted trajectories avoided high-strain zones and remained dynamically feasible. While some shape deviations occurred in constrained anatomies, the system maintained smooth and plausible motion across patient variants. Compared to a baseline, our method produced smoother trajectories with reduced peak accelerations. Unlike prior systems that rely on patient-led adaptation, our approach enables fully robot-driven motion generalization, making it particularly suitable for early-stage rehabilitation where patients cannot safely or actively control movement.

While this study focuses on a single muscle and 2D motion, the framework establishes a foundation for future extensions to 3D movement and multi-muscle safety adaptation. These results highlight the potential of strain-aware robot-led therapy to deliver safe and personalised rehabilitation, especially in early recovery stages when patients cannot actively participate.