Personalized Robotic Lumbar Rehabilitation Based on Medical-Imaging-Assisted Musculoskeletal Biomechanical Modeling

Low back pain often involves paraspinal muscle degeneration. Rehabilitation robots can provide high-consistency exercise therapy, but current technologies face the challenge of delivering personalized training to patients with various muscular conditions. To solve the issue, this work proposes an automated framework that translates sparse clinical magnetic resonance imaging (MRI) scans into personalized robotic lumbar rehabilitation strategies. First, a Bayesian fusion method with adaptive observation confidence is proposed to enable automatic posterior inference of the 3D paraspinal muscle geometry from sparse MRI. Key biomechanical parameters, including physiological cross-sectional area and fat infiltration, are extracted from the reconstructed muscle shapes to create a patient-specific musculoskeletal model. Based on the personalized model, a hierarchical optimization framework is developed to generate rehabilitation strategies of the combined multi-degree-of-freedom (multi-DOF) motions and dynamic interaction forces to maximize the target muscle activation. Validation on multi-center datasets demonstrates 90% dice similarity for the muscle reconstruction. Personalized validation experiments on volunteers with varying muscle fat infiltration levels revealed that conventional empirical force strategies failed to adapt to individual differences, leading to risks of activation overload or insufficient stimulation. In contrast, the proposed personalized strategy reduced the activation level variance by 60.49% compared to the empirical strategy and maintained the target activation error within 4%. The results demonstrate that the proposed framework significantly mitigates individual uncertainties, ensuring both safety and effectiveness in robotic rehabilitation.