A major challenge in the assessment of intersegmental spinal column angles during trunk motion is the inherent error in recording the movement of bony anatomical landmarks caused by soft tissue artifacts (STAs). This study aims to perform an uncertainty analysis and estimate the typical errors induced by STA into the intersegmental angles of a multisegment spinal column model during trunk bending in different directions by modeling the relative displacement between skin-mounted markers and actual bony landmarks during trunk bending. First, we modeled the maximum displacement of markers relative to the bony landmarks with a multivariate Gaussian distribution. In order to estimate the distribution parameters, we measured these relative displacements on five subjects at maximum trunk bending posture. Then, in order to model the error depending on trunk bending angle, we assumed that the error grows linearly as a function of the bending angle. Second, we applied our error model to the trunk motion measurement of 11 subjects to estimate the corrected trajectories of the bony landmarks and investigate the errors induced into the intersegmental angles of a multisegment spinal column model. For this purpose, the trunk was modeled as a seven-segment rigid-body system described using 23 reflective markers placed on various bony landmarks of the spinal column. Eleven seated subjects performed trunk bending in five directions and the three-dimensional (3D) intersegmental angles during trunk bending were calculated before and after error correction. While STA minimally affected the intersegmental angles in the sagittal plane (<16%), it considerably corrupted the intersegmental angles in the coronal (error ranged from 59% to 551%) and transverse (up to 161%) planes. Therefore, we recommend using the proposed error suppression technique for STA-induced error compensation as a tool to achieve more accurate spinal column kinematics measurements. Particularly, for intersegmental rotations in the coronal and transverse planes that have small range and are highly sensitive to measurement errors, the proposed technique makes the measurement more appropriate for use in clinical decision-making processes.

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