Evidence map›Paper›PMID 42821089›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Markerless motion capture to quantify disability and functional performance in spine patients: a concurrent validity study bridging laboratory biomechanics and clinical practice.

Ram Haddas, Ye Shu, Prasanth Romiyo, Gabriel Ramirez, Paul Rubery, Varun Puvanesarajah

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

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6 authors.

Ram HaddasUniversity of Rochester Medical Center, Rochester, USA. Ram_Haddas@URMC.Rochester.edu.ORCID https://orcid.org/0000-0002-1273-5499
Ye ShuUniversity of Rochester Medical Center, Rochester, USA.ORCID https://orcid.org/0009-0009-4359-1393
Prasanth RomiyoUniversity of Rochester Medical Center, Rochester, USA.
Gabriel RamirezUniversity of Rochester Medical Center, Rochester, USA.
Paul RuberyUniversity of Rochester Medical Center, Rochester, USA.
Varun PuvanesarajahUniversity of Rochester Medical Center, Rochester, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObjective assessment of functional mobility is helpful in evaluating disability and treatment outcomes in patients with degenerative spine disease. Traditional marker-based motion capture offers laboratory-grade accuracy but is limited by complex setup, soft-tissue artifact, and practical challenges in clinical settings. Consequently, there is a gap in scalable, accurate, and clinically feasible methods to objectively assess spine-related functional performance in real-world settings. Markerless motion capture, powered by computer vision and deep-learning algorithms, enables marker-free, rapid, and reproducible assessment of three-dimensional kinematics in clinic settings. PURPOSE: To evaluate the concurrent validity of a markerless motion capture system relative to a widely accepted reference method (marker-based motion capture) during gait and balance tasks in patients with spine pathology.

methodsA total of 116 participants (93 spine patients, 23 healthy controls) performed standardized gait and balance tasks captured simultaneously by marker-based and markerless systems. Agreement was examined using intraclass correlation coefficients (ICC), Bland-Altman analysis, root mean square deviation (RMSD), and Pearson correlation.

resultsAcross 580 gait and 348 balance trials, markerless motion capture demonstrated good to excellent agreement (ICC > 0.75) for trunk, lumbar, pelvic, and lower-extremity kinematics, with RMSD values ≤ 5°. Dynamic sagittal vertical axis (SVA) exhibited excellent reliability (ICC = 0.90, 95% CI: 0.84-0.94). Bland-Altman analysis showed minimal systematic bias. Balance assessments revealed excellent agreement for head sway in sagittal and coronal planes (ICC ≥ 0.97). However, transverse-plane rotations at the hip, knee, and ankle demonstrated poor agreement (ICC < 0.50), indicating current limitations for these specific parameters.

conclusionsMarkerless motion capture provides quantifiable, accurate, and clinically applicable measures of spine-related movement. Its efficiency, scalability, and accuracy support objective evaluation of disability, enable individualized rehabilitation, and facilitate longitudinal outcome monitoring, highlighting its potential for integration into standard clinical workflows and bridging the gap between laboratory-grade biomechanics and routine spine care.

Indexed as

Gait analysisLumbar degenerative diseaseMarkerless motion captureRehabilitation outcomesSpine biomechanics

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.