In this work, we propose a method for monitoring and managing rotator-cuff (RC) tendon strains in human-robot collaborative physical therapy for shoulder rehabilitation. We integrate a high-resolution biomechanical model with a collaborative industrial robot arm and an impedance controller to provide feedback to a human subject, therapist or both, which prevents the subject from entering unsafe poses during rehabilitation. The biomechanical model estimates RC tendon strain as a function of human shoulder configuration, muscle activation and applied external forces. Subject- and injury-specific data are model estimates of strain that compose strain maps, which capture the relationship between the RC strains and movement of the shoulder degrees of freedom (DoF). High-strain regions of the strain map are identified as unsafe zones by clustering and ellipse fitting to smoothly demarcate these zones. These unsafe areas, which reflect increased risks of (re-)injury, are used to define parameters of an impedance controller and reference pose for real-time biomechanical safety control. Using strain maps we demonstrate both safe patient-led movements and teleoperated movements that prevent the subject from entering unsafe zones. In the teleoperated case, the physical therapist leads the patient remotely using a haptic device. The proposed method has the potential to improve the safety, range of motion, and volume of activity that a patient receives through robot-mediated physical therapy. We validated our approach using three experiments that demonstrate shoulder joint torques of less than 1 Nm during free motion with larger torques occurring only when the subject was asked to actively push into the unsafe boundary or, in the case of teleoperation, to resist the physical therapist.

In this work, we explore using computational musculoskeletal modeling to equip an industrial collaborative robot with awareness of the internal state of a patient to safely deliver physical therapy. A major concern of robot-mediated physical therapy is that robots may unwittingly injure patients. For patients with shoulder injuries this typically means the risk of tearing a rotator-cuff muscle tendon. Risk of reinjury hampers both human and robot therapists and it is the main reason for conservative physical therapy. Advances in human musculoskeletal modeling, however, can equip robots with additional perception of potential reinjury risks. While the ultimate goal is to improve the safety, range-of-motion and activity that patients receive through robot-mediated therapy, the aim of this letter is to develop and test a framework that enables the robot to understand the state of the patient and to execute physical therapy movements that demonstrate low injury risk and achieve a large range-of-motion in human subjects. We build on prior work in human-robot interaction via impedance control, but take robot awareness of the human to the next level by including and manipulating a musculoskeletal model in parallel to the patient. Taking the most common shoulder impairments (i.e., rotator-cuff tears) as an example, we demonstrate planned, model-based trajectories that minimize strain in these muscles and corresponding robot-mediated movements on healthy subjects. Our experiments suggest that musculoskeletal awareness is a promising approach to plan and deliver therapeutic movements that are safe and effective via an industrial robot.