Network analysis of surface deformation reveals trunk modularity and synchronization during gait.

Understanding the dynamic behavior of the human trunk during locomotion is critical for addressing prevalent disorders like low back pain and scoliosis, yet current biomechanical models often oversimplify the trunk as rigid segments. To bridge this gap, we introduce a novel approach combining body surface topography changes with network analysis to characterize trunk motion as a dynamic continuum. By employing the skin surface as a non-invasive observer, we utilized community detection algorithms to identify synchronous deformation regions (SDRs) during gait. Our results suggest that the human back operates as a modular system of synchronized kinematic regions rather than a single homogeneous tissue. These SDRs exhibit robust spatial boundaries and long-range synergies across varying walking speeds, reflecting underlying musculoskeletal dynamics, including spinal kinematics and myofascial coordination. We identified stable clustering in thoracic and lumbar regions, alongside speed-dependent pelvic-scapular synchronization, validating the synergy between upper and lower limb girdles. This framework establishes a methodological foundation for precision rehabilitation by observing the motion of the outer skin. In the future, after validation in larger cohorts and clinical populations, this approach may support candidate surface-derived indicators for evaluating pathological deviations, such as asymmetry in scoliosis or rigid movement patterns in low back pain.