Predicting cervico-thoraco-lumbar vertebra positions from cutaneous markers: Combining local frame and postural predictors improves robustness to posture.
J Biomech 2024;
164:111961. [PMID:
38310767 DOI:
10.1016/j.jbiomech.2024.111961]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 11/21/2023] [Accepted: 01/19/2024] [Indexed: 02/06/2024]
Abstract
Predictions of vertebra positions from external data are required in many fields like motion analysis or for clinical applications. Existing predictions mainly cover the thoraco-lumbar spine, in one posture. The objective of this study was to develop a method offering robust vertebra position predictions in different postures for the whole spine, in the sagittal plane. EOS radiographs were taken in three postures: slouched, erect, and subject's usual sitting posture, using 21 healthy participants pre-equipped with opaque cutaneous markers. Local curvilinear Frenet frames were built on a spline fitted to spinous processes' cutaneous markers. Vertebra positions were expressed as polar coordinates in these frames, defining an angle (α) and distance (d). Multilinear regressions were fitted to explain α and d from anthropometric predictors and predictors presumed to be linked to spinal posture, the predictors' effects being considered both locally and remotely. Anthropometric predictors were the main predictors for d distances, and postural predictors for α angles, with postural predictors still showing a marked influence on d distances for the cervical spine. Vertebra positions were then predicted by cross-validation. The average RMSE on vertebra positions was 11.0 ± 3.7 mm across the entire spine, 13.4 ± 4.1 mm across the cervical spine and 10.1 ± 3.1 mm across the thoraco-lumbar spine for all participants and postures, performances similar to previous models designed for a single posture. Our simple geometrical and statistical model thus appears promising for predicting vertebra positions from external data in several spinal postures and for the whole spine.
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