Monocular markerless biomechanics for clinically interpretable gait assessment in spinal cord injury

Shreyasvi Natraj; Mathieu Ruepp; Yanke Li; Robert Riener; Inge Eriks-Hoogland; Diego Paez-Granados

cs.CVOct 4, 2026

Monocular markerless biomechanics for clinically interpretable gait assessment in spinal cord injury

Authors: Shreyasvi Natraj, Mathieu Ruepp, Yanke Li, Robert Riener, Inge Eriks-Hoogland, Diego Paez-Granados

Organizations: Spinal Cord Artificial Intelligence (SCAI) Lab, ETH Zürich, Zürich, Switzerland · Swiss Paraplegic Research, Swiss Paraplegic Centre, Nottwil, Switzerland · University Hospital Balgrist, Zurich, Switzerland · Graduate School of Engineering, Tohoku University, Sendai, Japan · Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland · Swiss Paraplegic Centre, Nottwil, Switzerland · University of Tsukuba, 305-8577, Ibaraki, Tsukuba, Japan

Abstract

Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which few clinics have. Monocular markerless pipelines have been established in fewer healthy adult cohorts but not in neurological cohorts. We present the SCAI SCI Gait dataset, comprising 239 adult individuals with spinal cord injury with synchronized video, motion capture, and force-plate measurements, we fitted a parametric body mesh to a single sagittal-view video, driving an anthropometrically scaled OpenSim model via virtual markers. Markerless lower-body kinematics showed state-of-the-art agreement with motion-capture measurements (r = 0.68-0.90, p < 0.001, and RMSE = 4.18-6.49 degrees), and accurate kinematics-based predicted ground-reaction forces closely matched those measured by force plates (r = 0.85-0.87, p < 0.001, and RMSE = 2.13-2.19 Newton per kg). Furthermore, conditional-dependence graph analysis with Markov blankets revealed that waveform components were conditionally associated with functional independence, and speed-stratified clustering revealed distinct mechanical strategies among individuals walking at similar speeds. These findings establish the use of monocular video as a scalable approach for clinically meaningful biomechanical assessment and data-driven phenotyping in patients with spinal cord injury. Github: https://github.com/SCAI-Lab/SCAI-SCI-Gait

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May 11, 2026, cs.CVJ/K move · Enter open · S save

Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort

Lauhitya Reddy, Seth Donahue, Jeremy Bauer, Susan Sienko+7

Department of Biomedical Informatics, Emory University, Atlanta, GA, USA · Shriners Children’s, USA · The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA

Gait Analysis Quantifying

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Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood. Approximately 75% of children with CP are ambulatory, and accurate gait assessment is central to preserving walking function, which deteriorates by mid-adulthood in a quarter to half of adults with CP. The Rodda and Graham classification system quantifies sagittal-plane gait deviations using ankle and knee z-scores derived from 3D Instrumented Gait Analysis (3D-IGA), but 3D-IGA is expensive and limited to specialized centers, while observational assessment shows only moderate inter-rater agreement. We developed a markerless gait analysis pipeline that quantifies Rodda and Graham knee and ankle z-scores directly from single-view clinical gait videos. Across 1,058 bilateral limb samples from 529 trials of 152 children (88 male, 63 female; age 12.1

\pm

4.0 years; 60 distinct primary diagnoses, cerebral palsy the most common at

n=54

), the sagittal-view model achieved

R^2 = 0.80 \pm 0.02

and CCC

= 0.89 \pm 0.02

for knee z-scores and

R^2 = 0.57 \pm 0.02

and CCC

= 0.72 \pm 0.02

for ankle z-scores against 3D-IGA. Binary screening for excess knee flexion achieves AUROC

= 0.88

, correctly identifying 83% of affected children, and applying Rodda and Graham rules yields

43 \pm 1

% 7-class accuracy with macro-AUROC

= 0.78 \pm 0.01

, ankle prediction error remaining the primary bottleneck. Beyond cross-sectional screening, continuous z-scores support longitudinal trajectory tracking across visits, providing a quantitative substrate for monitoring disease progression and treatment response unavailable from observational scales. These results demonstrate the feasibility of video-based z-score estimation, excess-flexion screening, and longitudinal trajectory tracking as a path toward scalable, objective gait assessment in low-resource clinical settings.

Abstract

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