MajidiRad, AmirHossein

Rotator cuff injuries are among the most common musculoskeletal conditions that affect shoulder function and can ultimately impact quality of life. While physical therapy is essential in the care of rotator cuff injuries, the ideal dose of therapeutic exercises continues to be a significant clinical dilemma because of the generalized nature of rehabilitation protocols. This pilot study proposes a machine learning approach to personalize rehabilitation using surface electromyography (sEMG) data collected from eight healthy individuals by testing four key shoulder movements: scaption, internal rotation, external rotation, and external rotation at 90° abduction. In this research, the XGBoost algorithm was used to model muscle activation patterns by achieving a high predictive accuracy (R2 = 0.5325; MSE = 0.0084 μV2). Because sEMG reliably measures superficial muscle activity, a linear programming model was used to divide a 60 min therapy session in a way that increases activation of superficial muscles (such as deltoid and trapezius) while reducing strain on deep muscles (such as supraspinatus and infraspinatus). Three optimization scenarios were tested by reflecting a different clinical goal: prioritizing superficial muscles, minimizing deep muscle strain, or balancing both. Optimized time allocations assigned more time to external rotation at 90° abduction and scaption. This research demonstrates the potential for data-driven methods to transform rotator cuff rehabilitation through personalized and evidence-based treatment plans. The results enhance clinical practice by enabling adaptive rehabilitation planning and show that machine learning can support decision-making in complex muscle activation analysis with strong performance and low latency.

Abstract Rotator cuff tears are among the most common shoulder injuries, making them an attractive topic of study. Yet, there is a lack of clear understanding of attributes associated with deep and superficial muscles when it is intended to study the recovery progress after rehabilitation. Through a systematic approach, this project investigates the activation of rotator cuff muscles using surface electromyography (sEMG) sensors. The team developed a strategic plan to investigate the precision of the NORAXON technology that is utilized in this study to ensure that it is capable of capturing small variations in shoulder motion. This is followed by a reliability analysis to ascertain reproducibility of the experiment. The levels of muscle activities for superficial and deep muscles are monitored to explore the impact of traditionally prescribed arm movements used for the rehabilitation of rotator cuff injuries. Furthermore, an analysis of forces generated in the muscles is done to provide better insight into the activation levels of rotator cuff muscles. The results were promising, with a competent technology and acceptable correlation associated with muscle activities across all subjects. Four common arm movements were studied; scaption generated a significant response in the targeted muscles, particularly of the supraspinatus. Results were conclusive across all subjects, and simulation also supported the experiment showing high activation levels for the supraspinatus. The teres minor also showed a significant contribution when performing external rotation at 90⁰ abduction. The outcome of this study is significant for this ongoing project as it identifies the most impactful exercises that ensure targeted muscles are being triggered. It also lays the groundwork for expanding the project to a clinical study focusing on the most effective arm movements in order to improve rehabilitation process and reduce pertinent cost.