arXiv subjects

Dennis Perchthaler

Publications and source records attributed to Dennis Perchthaler.

2 recordsLinked to original sources

arXiv2026

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

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.

cs.CE↗

arXiv2025

Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning

Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to alignment and its capacity for robust outlier detection remains unclear. This study compares an SPM approach with an explainable machine learning (ML) approach to establish transparent quality-control pipelines for plantar pressure datasets. Data from multiple centers were annotated by expert consensus and enriched with synthetic anomalies resulting in 798 valid samples and 2000 outliers. We evaluated (i) a non-parametric, registration-dependent SPM approach and (ii) a convolutional neural network (CNN), explained using SHapley Additive exPlanations (SHAP). Performance was assessed via nested cross-validation; explanation quality via a semantic differential survey with domain experts. The ML model reached high accuracy and outperformed SPM, which misclassified clinically meaningful variations and missed true outliers. Experts perceived both SPM and SHAP explanations as clear, useful, and trustworthy, though SPM was assessed less complex. These findings highlight the complementary potential of SPM and explainable ML as approaches for automated outlier detection in plantar pressure data, and underscore the importance of explainability in translating complex model outputs into interpretable insights that can effectively inform decision-making.

cs.AI↗