Optimizing Rotator Cuff Rehabilitation Dosage Using a Machine Learning–Based Pilot Study with EMG for Personalized Physical Therapy

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 = 0.9875; MSE = 15.0983 μV2). Because sEMG reliably measures superficial muscle activity, a linear programming model was used to divide a 60-minute 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 are a step toward improving clinical practices by providing physical therapists with tools for more effective and adaptive rehabilitation planning. These findings suggest that a machine learning approach could play a valuable role in supporting clinical decision making specifically in applications such as the analysis of complicated muscle activation patterns. Its strong predictive performance and computational efficiency identify machine learning as a promising candidate and can be deployed in clinical workflows where reliability and speed plays an important role.