Biomechanics Aware Collaborative Robot System for Delivery of Safe Physical Therapy in Shoulder Rehabilitation

Abstract

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., rotatorcuff tears) as an example, we demonstrate planned, modelbased 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.

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

“…Our study has broader implications for musculoskeletal models of the shoulder and related solvers for clinical and rehabilitation applications. For example, shoulder surgery [ 48 ], robotic-led shoulder physiotherapy [ 55 , 56 ], and ergonomics during manual work [ 57 , 58 ] all rely on the accuracy and speed of force/activity estimates from a musculoskeletal model. Our study contributes significantly to these types of applications by providing free, open-source models and tools to accurately evaluate shoulder function in close to real-time.…”

The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task

“…Robotics is a viable alternative for physical guidance, and robotic systems have been used in fields such as rehabilitation [ 23 , 24 ], physiotherapy [ 25 ], human skill learning [ 26 , 27 , 28 ], human–human coordination [ 29 , 30 ], and human motor control studies [ 31 , 32 , 33 ]. In recent years, the field of sensorimotor learning has witnessed significant advancements, particularly in the use of haptic devices for enhancing learning and skill acquisition.…”

A Robot-Assisted Framework for Rehabilitation Practices: Implementation and Experimental Results

“…Kyrkjebø et al [ 13 ] analyzed the potential of the UR5, a collaborative robot from Universal Robots, for the rehabilitation of stroke patients. Gherman et al [ 14 ] envisaged the use of a cobot for upper limb rehabilitation, while Prendergast et al [ 15 ] focused on shoulder diseases. For further details on the use of cobotics in the rehabilitation scenario, see [ 16 , 17 , 18 , 19 , 20 , 21 ].…”