Search arXivSearch

arXiv · 2608.12945

Spinal Coupling in Frontal and Transversal Plane During Gait - A Segmental and Time-Dependent Analysis of the Thoracic and Lumbar Spine

Abstract

Introduction: Coupling between lateral deviation and axial rotation is a known feature of spinal mechanics, yet its behavior at the individual vertebral level during gait, as well as the association with sagittal posture remains poorly understood. Methods: This study analyzed spinal kinematics in a diverse cohort (n=642) using a non-invasive rasterstereography system with an instrumented treadmill, quantifying time-dependent coupling of rotation and lateral deviation for each vertebra from T3 to L4 during walking as well as the influence of sagittal posture on this coupling. Coupling behavior was analyzed with absolute phase lag, normalized signed area and tilt angle derived from the Fourier series. Results: Results revealed cranio-caudal patterns for all three metrics with differences between almost all adjacent vertebrae and different turning points, i.e., where the coupling behavior changed (from increase to decrease and vice versa). Generalized, as well as pooled static sagittal posture significantly modulated these patterns, while individual deviations from static sagittal posture influenced the metrics. Conclusion: These findings provide the first dynamic, vertebra-level characterization of spinal coupling during gait. Furthermore, they offer a foundation for an understanding of spinal biomechanics and the influence of static sagittal posture, potentially relevant to the diagnosis and treatment of spinal disorders.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jonas Dully, Carlo Dindorf, Henriette Rönsch, Dennis Perchthaler, Jürgen Konradi, Ulrich Betz, Michael Fröhlich. 2026-08-14. Spinal Coupling in Frontal and Transversal Plane During Gait - A Segmental and Time-Dependent Analysis of the Thoracic and Lumbar Spine. https://arxiv.org/abs/2608.12945

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Cosm: Collective switched motion for sparse Ising optimization

We introduce Collective Switched Motion (Cosm), a heuristic optimization framework based on switched collective dynamics. Cosm compiles the objective into subobjectives that are activated sequentially, temporally separating competing local influences. The interplay of switching and local interactions among variables gives rise to collective search behavior, while a new correlated perturbation mechanism encourages coordinated cluster motion. Tests on tuned-hardness spin-glass benchmarks suggest more favorable algorithmic scaling than that reported for leading dynamical solvers. Cosm heuristically attains the certified optima of three of the largest Gset instances (G72, G77, G81), exceeding the previously reported heuristic solutions. On the large random-graph instances G61 and G70, a CPU implementation reliably attains the best-known solutions, reaching cuts of 5799 and 9595 with 99%-confidence times-to-target of 15 s and 2.4 s, respectively. On the heterogeneous-degree G64 instance, Cosm establishes a new best-known cut of 8753. Broadly, the results suggest an alternative approach to heuristic design in which local dynamics and orchestration mechanisms are carefully designed so that effective search emerges.

cs.CE

Decomposing Firm-Level Crisis Responses from Incomplete Market Signals: Evidence from China's IT Sector During COVID-19

Exogenous shocks generate heterogeneous behavioral responses across firms, yet event studies typically report only sector-level averages. This paper develops a multi-method approach combining causal identification (difference-in-differences with cluster-robust inference), unsupervised behavioral discovery (K-means trajectory clustering, Gaussian hidden Markov models), and cross-sectional resilience prediction (logistic regression with nested cross-validation) to decompose firm-level response heterogeneity from noisy market signals. We demonstrate the approach on 246 Chinese A-share IT firms (216 with complete data for all analyses) during the COVID-19 shock (January 2020), using 252 non-IT CSI 300 firms as controls. The return decline was market-wide, not IT-specific (DID p = 0.59); the IT-specific effect was elevated volatility (DID beta = 0.043, cluster-robust p < 0.001), with the effect surviving Benjamini-Hochberg correction in 13 of 30 alternative specifications. Unsupervised clustering produced three trajectory groups: fast recovery (36 companies, +29.7%), resilient/moderate (67 companies), and persistent drag (113 companies, -6.9%). Pre-crisis financial fundamentals showed only modest predictive power for resilience (nested CV AUC = 0.635, 95% CI: 0.558-0.711; permutation p = 0.016), consistent with the limited informativeness of publicly available signals for anticipating crisis outcomes. The combination of causal analysis, unsupervised learning, and prediction represents a reproducible framework which can be applied to crises in other market periods.

cs.CE

An Insurance Broker for Every Small Business: The Economics of Exceptional Care at Scale

Small-business owners need expert guidance on their own terms, across schedules, languages, and channels, but low premiums make exceptional, continuous human service uneconomic for much of the market. Combining public evidence, Kinro operational data, and an illustrative five-year service model, we show why traditional brokerage economics leave 35 million U.S. small businesses underserved. An AI-native brokerage can change those economics by performing and coordinating routine work continuously, while licensed professionals govern consequential exceptions and the brokerage remains accountable.

cs.CE