Enabling Patient- and Teleoperator-led Robotic Physiotherapy via Strain Map Segmentation and Shared-authority

Abstract

In this work, we propose a method for monitoring and managing rotator-cuff (RC) tendon strains in humanrobot 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 injuryspecific 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). Highstrain 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.

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.…”

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.…”

Human Modeling in Physical Human-Robot Interaction: A Brief Survey

“…Other examples include patient-specific musculoskeletal models integrated into real-time robot control to guide rehabilitation therapy, which could be done either by a robot autonomously [82], [83] or by a therapist through teleoperation [84], [85]. A further categorization can be made based on the type of assistive robot: exoskeleton [79]- [81] and external robotic manipulator [2], [84], [85].…”

“…Other examples include patient-specific musculoskeletal models integrated into real-time robot control to guide rehabilitation therapy, which could be done either by a robot autonomously [82], [83] or by a therapist through teleoperation [84], [85]. A further categorization can be made based on the type of assistive robot: exoskeleton [79]- [81] and external robotic manipulator [2], [84], [85]. For instance, The work in [2] proposed to use a novel strain map that exploited latent space to abstract musculoskeletal model data related to tissue functions into an intuitive map for safe physical therapy through pHRI.…”