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This study aims to develop and evaluate HumanMoveNet, a novel digital framework that reconstructs a temporally consistent 3D human model with precise spinal curvature from monocular visual data to enable objective LBP screening and rehabilitation assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed hybrid framework integrates static anatomical reconstruction, dynamic pose estimation, and temporal smoothing. From a human gait video, a 3D reconstruction network first generates a static human model with personalized spinal morphology. The gait video is then processed via optimized 2D pose estimation and parametric model regression to obtain frame-by-frame 3D human meshes. Graph convolutional and long short-term memory networks are employed to ensure temporal motion continuity. Finally, the static spine is fused with the dynamic pose sequence to create a \u201cdynamic spine,\u201d from which key biomechanical parameters\u2014lumbar range of motion (ROM), pelvic tilt range, and spinal symmetry index\u2014are extracted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Validation on 146 subjects demonstrated superior reconstruction performance, achieving a 5.6% improvement (18.85 vs 17.85) in Peak Signal-to-Noise Ratio (PSNR), a 28.4% reduction in Hausdorff Distance (2.1126 mm vs 2.9505 mm), and a 5.1% increase in Intersection over Union (IoU) (0.4122 vs 0.3921) compared with state-of-the-art methods. Analysis of spinal curvature variation showed\n                      <jats:inline-formula>\n                        <jats:tex-math>$$\\Delta \\kappa $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      values of\n                      <jats:inline-formula>\n                        <jats:tex-math>$$2.15 \\pm 0.52^\\circ $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      in females and\n                      <jats:inline-formula>\n                        <jats:tex-math>$$1.98 \\pm 0.61^\\circ $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      in males, with no significant gender difference (\n                      <jats:inline-formula>\n                        <jats:tex-math>$$p = 0.066$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ). Gender-specific analysis further revealed that females had greater pelvic mobility (\n                      <jats:inline-formula>\n                        <jats:tex-math>$$6.5 \\pm 2.1^\\circ $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      vs\n                      <jats:inline-formula>\n                        <jats:tex-math>$$5.2 \\pm 1.8^\\circ $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ,\n                      <jats:inline-formula>\n                        <jats:tex-math>$$p &lt; 0.05$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ) and lumbar ROM (\n                      <jats:inline-formula>\n                        <jats:tex-math>$$18.5 \\pm 4.2^\\circ $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      vs\n                      <jats:inline-formula>\n                        <jats:tex-math>$$16.8 \\pm 4.7^\\circ $$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ,\n                      <jats:inline-formula>\n                        <jats:tex-math>$$p = 0.024$$<\/jats:tex-math>\n                      <\/jats:inline-formula>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>HumanMoveNet provides a precise, label-free solution for assessing spinal dynamic function using conventional visual data. By combining high-fidelity spinal anatomy with dynamic motion analysis, it effectively captures LBP-related movement alterations, and shows strong potential for community-based screening, rehabilitation evaluation, and personalized care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00586-026-09898-x","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T08:02:49Z","timestamp":1777881769000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HumanMoveNet: a dynamic 3D spine reconstruction framework for low back pain screening and rehabilitation assessment"],"prefix":"10.1007","author":[{"given":"Tao","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason P. 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