Sensitivity of intersegmental angles of the spinal column to errors due to marker misplacement

A Self-Contained 3D Biomechanical Analysis Lab for Complete Automatic Spine and Full Skeleton Assessment of Posture, Gait and Run

Quantitative functional assessment of Posture and Motion Analysis of the entire skeleton and spine is highly desirable. Nonetheless, in most studies focused on posture and movement biomechanics, the spine is only grossly depicted because of its required level of complexity. Approaches integrating pressure measurement devices with stereophotogrammetric systems have been presented in the literature, but spine biomechanics studies have rarely been linked to baropodometry. A new multi-sensor system called GOALS-E.G.G. (Global Opto-electronic Approach for Locomotion and Spine-Expert Gait Guru), integrating a fully genlock-synched baropodometric treadmill with a stereophotogrammetric device, is introduced to overcome the above-described limitations. The GOALS-EGG extends the features of a complete 3D parametric biomechanical skeleton model, developed in an original way for static 3D posture analysis, to kinematic and kinetic analysis of movement, gait and run. By integrating baropodometric data, the model allows the estimation of lower limb net-joint forces, torques and muscle power. Net forces and torques are also assessed at intervertebral levels. All the elaborations are completely automatised up to the mean behaviour extraction for both posture and cyclic-repetitive tasks, allowing the clinician/researcher to perform, per each patient, multiple postural/movement tests and compare them in a unified statistically reliable framework.

Optimal Estimation of Anthropometric Parameters for Quantifying Multisegment Trunk Kinetics

Kinetics assessment of the human head-arms-trunk (HAT) complex via a multisegment model is a useful tool for objective clinical evaluation of several pathological conditions. Inaccuracies in body segment parameters (BSPs) are a major source of uncertainty in the estimation of the joint moments associated with the multisegment HAT. Given the large intersubject variability, there is currently no comprehensive database for the estimation of BSPs for the HAT. We propose a nonlinear, multistep, optimization-based, noninvasive method for estimating individual-specific BSPs and calculating joint moments in a multisegment HAT model. Eleven nondisabled individuals participated in a trunk-bending experiment and their body motion was recorded using cameras and a force plate. A seven-segment model of the HAT was reconstructed for each participant. An initial guess of the BSPs was obtained by individual-specific scaling of the BSPs calculated from the male visible human (MVH) images. The intersegmental moments were calculated using both bottom-up and top-down inverse dynamics approaches. Our proposed method adjusted the scaled BSPs and center of pressure (COP) offsets to estimate optimal individual-specific BSPs that minimize the difference between the moments obtained by top-down and bottom-up inverse dynamics approaches. Our results indicate that the proposed method reduced the error in the net joint moment estimation (defined as the difference between the net joint moment calculated via bottom-up and top-down approaches) by 79.3% (median among participants). Our proposed method enables an optimized estimation of individual-specific BSPs and, consequently, a less erroneous assessment of the three-dimensional (3D) kinetics of a multisegment HAT model.

Multisegment Kinematics of the Spinal Column: Soft Tissue Artifacts Assessment

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.