Shoulder Range of Motion Rehabilitation Robot Incorporating Scapulohumeral Rhythm for Frozen Shoulder

๐Ÿ“… 2025-04-14
๐Ÿ“ˆ Citations: 0
โœจ Influential: 0
๐Ÿ“„ PDF
๐Ÿค– AI Summary
Compensatory shoulder elevation during passive rehabilitation of adhesive capsulitis (frozen shoulder) compromises therapeutic efficacy. Method: This study proposes a 6-degree-of-freedom shoulder rehabilitation robot featuring a novel single-degree-of-freedom scapular press mechanismโ€”the first to actively suppress scapular elevation and other compensatory motions in robotic rehabilitation. Integrating contralateral motion recording and playback control, we develop a biomechanics-informed scapulohumeral rhythm (SHR) coordination algorithm and a personalized two-phase operation paradigm. Results: Experimental evaluation demonstrates motion playback RMSE < 1ยฐ; under simulated frozen shoulder conditions, the system significantly delays onset of compensatory shrugging and improves adherence of affected-side abduction/flexion trajectories to normative SHR patterns. This work establishes a novel paradigm and key enabling technologies for precise, rhythm-aware passive rehabilitation of adhesive capsulitis.

Technology Category

Application Category

๐Ÿ“ Abstract
This paper presents a novel rehabilitation robot designed to address the challenges of passive range of motion (PROM) exercises for frozen shoulder patients by integrating advanced scapulohumeral rhythm stabilization. Frozen shoulder is characterized by limited glenohumeral motion and disrupted scapulohumeral rhythm, with therapist-assisted interventions being highly effective for restoring normal shoulder function. While existing robotic solutions replicate natural shoulder biomechanics, they lack the ability to stabilize compensatory movements, such as shoulder shrugging, which are critical for effective rehabilitation. Our proposed device features a 6 degrees of freedom (DoF) mechanism, including 5 DoF for shoulder motion and an innovative 1 DoF Joint press for scapular stabilization. The robot employs a personalized two-phase operation: recording normal shoulder movement patterns from the unaffected side and applying them to guide the affected side. Experimental results demonstrated the robot's ability to replicate recorded motion patterns with high precision, with root mean square error (RMSE) values consistently below 1 degree. In simulated frozen shoulder conditions, the robot effectively suppressed scapular elevation, delaying the onset of compensatory movements and guiding the affected shoulder to move more closely in alignment with normal shoulder motion, particularly during arm elevation movements such as abduction and flexion. These findings confirm the robot's potential as a rehabilitation tool capable of automating PROM exercises while correcting compensatory movements. The system provides a foundation for advanced, personalized rehabilitation for patients with frozen shoulders.
Problem

Research questions and friction points this paper is trying to address.

Develops a robot for frozen shoulder rehabilitation with scapulohumeral rhythm stabilization
Addresses compensatory movement issues in existing robotic rehabilitation solutions
Personalizes therapy by replicating normal shoulder motion from the unaffected side
Innovation

Methods, ideas, or system contributions that make the work stand out.

6 DoF mechanism with scapular stabilization
Personalized two-phase operation system
Precise motion replication with low RMSE
๐Ÿ”Ž Similar Papers
No similar papers found.
H
Hyunbum Cho
Department of Intelligence and Information, Seoul National University, Seoul, 08826, Republic of Korea; Blue Robin inc., Seoul, 06524, Republic of Korea
S
Sungmoon Hur
Department of Intelligence and Information, Seoul National University, Seoul, 08826, Republic of Korea; Blue Robin inc., Seoul, 06524, Republic of Korea
Joowan Kim
Joowan Kim
Seoul national university
Visual SLAMPerception
Keewon Kim
Keewon Kim
Seoul National University Hospital
neurophysiologyelectromyographyintraoperative monitoringmotion analysismusculoskeletal rehabilitation
Jaeheung Park
Jaeheung Park
Seoul National University
Robotics