Low back pain (LBP) is a prevalent and disabling musculoskeletal disorder that severely affects patients’ quality of life. Traditional rehabilitation training relies on therapists’ subjective observations, which have issues such as imprecise assessment and untimely feedback. A deep learning-based motion analysis system was developed to deliver an intelligent exercise training program with real-time, precise feedback. Through a randomized controlled trial (single-center design), 118 patients with chronic non-specific LBP were randomly assigned to an experimental group (receiving intelligent training based on the motion analysis system) and a control group (receiving conventional guided training). Outcome measures included the visual analog scale (VAS), Oswestry disability index (ODI), surface electromyography (sEMG) signals of core muscles, and system-generated movement quality scores. System performance tests indicated that the pose estimation model achieved a mean Average Precision (AP@0.5) of 96.7% in keypoint detection, with a mean per joint position error (MPJPE) of 25.3 mm and a pelvic angle estimation error of 3.1°, providing a reliable technical foundation for precise motion analysis. Supported by this system, after the 8-week intervention (T1), improvements in both VAS scores (2.6 ± 1.1 vs. 3.9 ± 1.3) and ODI scores (18.7 ± 6.3 vs. 28.3 ± 7.6) were significantly greater in the experimental group compared to the control group (P < 0.05), and this advantage was maintained at the 1-month follow-up after the intervention ended (T2). The average movement quality score during the entire intervention period was also significantly higher in the experimental group (88.5±4.2 points) than in the control group (76.8±7.5 points). The sEMG results indicated more coordinated activation and less compensation in the core muscles of the experimental group. The training adherence rate was significantly higher in the experimental group (92.5%) compared to the control group (85.3%). The deep learning-based motion analysis system developed in this study is highly accurate and robust, capable of providing precise, real-time feedback for rehabilitation training for patients with LBP. It significantly alleviates pain, improves functional impairments, and optimizes movement patterns and neuromuscular control. This innovative rehabilitation method has good clinical application prospects.
Jinfeng Zhao (Tue,) studied this question.