ArticleAnnals of biomedical engineering2021
A Dynamic Optimization Approach for Solving Spine Kinematics While Calibrating Subject-Specific Mechanical Properties.
Article in Annals of biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- A stiffness-dependent method to estimate spine kinematics and loading reveals the impact of nonlinear mechanics during lifting tasks.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 · 2026Article
- Repeatability and agreement of planar lumbar range of motion measured by dynamic fluoroscopy and optical motion capture in an ovine cadaveric model.Frontiers in bioengineering and biotechnology · 2026Article
- Automated segmentation of trunk musculature with a deep CNN trained from sparse annotations in radiation therapy patients with metastatic spine disease: an observational study.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- Identification of a lumped-parameter model of the intervertebral joint from experimental data.Frontiers in bioengineering and biotechnology · 2024Article
- Variability of intervertebral joint stiffness between specimens and spine levels.Frontiers in bioengineering and biotechnology · 2024Article
- Dynamic segmental kinematics of the lumbar spine during diagnostic movements.Frontiers in bioengineering and biotechnology · 2023Article
- Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications.Applied sciences (Basel, Switzerland) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
This study aims to propose a new optimization framework for solving spine kinematics based on skin-mounted markers and estimate subject-specific mechanical properties of the intervertebral joints. The approach enforces dynamic consistency in the entire skeletal system over the entire time-trajectory while personalizing spinal stiffness. 3D reflective markers mounted on ten vertebrae during spine motions were measured in ten healthy volunteers. Biplanar X-rays were taken during neutral stance of the subjects wearing the markers. Calculated spine kinematics were compared to those calculated using inverse kinematics (IK) and IK with imposed generic kinematic constraints. Calculated spine kinematics compared well with standing X-rays, with average root mean square differences of the vertebral body center positions below 10.1 mm and below [Formula: see text] for joint orientation angles. For flexion/extension and lateral bending, the lumbar rotation distribution patterns, as well as the ranges of rotations matched in vivo literature data. The approach outperforms state-of-art IK and IK with constraints methods. Calculated ratios reflect reduced spinal stiffness in low-resistance zone and increased stiffness in high-resistance zone. The patterns of calibrated stiffness were consistent with previously reported experimentally determined patterns. This approach will further our insight into spinal mechanics by increasing the physiological representativeness of spinal motion simulations.
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Registered trials
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.