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The Influence of Kinematic Constraints on Model Performance During Inverse Kinematics Analysis of the Thoracolumbar Spine.

ABSTRACT: Motion analysis is increasingly applied to spine musculoskeletal models using kinematic constraints to estimate individual intervertebral joint movements, which cannot be directly measured from the skin surface markers. Traditionally, kinematic constraints have allowed a single spinal degree of freedom (DOF) in each direction, and there has been little examination of how different kinematic constraints affect evaluations of spine motion. Thus, the objective of this study was to evaluate the performance of different kinematic constraints for inverse kinematics analysis. We collected motion analysis marker data in seven healthy participants (4F, 3M, aged 27-67) during flexion-extension, lateral bending, and axial rotation tasks. Inverse kinematics analyses were performed on subject-specific models with 17 thoracolumbar joints allowing 51 rotational DOF (51DOF) and corresponding models including seven sets of kinematic constraints that limited spine motion from 3 to 9DOF. Outcomes included: (1) root mean square (RMS) error of spine markers (measured vs. model); (2) lag-one autocorrelation coefficients to assess smoothness of angular motions; (3) maximum range of motion (ROM) of intervertebral joints in three directions of motion (FE, LB, AR) to assess whether they are physiologically reasonable; and (4) segmental spine angles in static ROM trials. We found that RMS error of spine markers was higher with constraints than without (p < 0.0001) but did not notably improve kinematic constraints above 6DOF. Compared to segmental angles calculated directly from spine markers, models with kinematic constraints had moderate to good intraclass correlation coefficients (ICCs) for flexion-extension and lateral bending, though weak to moderate ICCs for axial rotation. Adding more DOF to kinematic constraints did not improve performance in matching segmental angles. Kinematic constraints with 4-6DOF produced similar levels of smoothness across all tasks and generally improved smoothness compared to 9DOF or unconstrained (51DOF) models. Our results also revealed that the maximum joint ROMs predicted using 4-6DOF constraints were largely within physiologically acceptable ranges throughout the spine and in all directions of motions. We conclude that a kinematic constraint with 5DOF can produce smooth spine motions with physiologically reasonable joint ROMs and relatively low marker error.

SUBMITTER: Alemi MM

PROVIDER: S-EPMC8358679 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A precise and comprehensive definition of "normal" in vivo cervical kinematics does not exist due to high intersubject variability and the absence of midrange kinematic data. In vitro test protocols and finite element models that are validated using only end range of motion data may not accurately reproduce continuous in vivo motion.The primary objective of this study was to precisely quantify cervical spine intervertebral kinematics during continuous, functional flexion-extension in asymptomatic subjects. The advantages of assessing continuous intervertebral kinematics were demonstrated by comparing asymptomatic controls with patients with single-level anterior arthrodesis.Cervical spine kinematics were determined during continuous in vivo flexion-extension in a clinically relevant age group of asymptomatic controls and a group of patients with C5-C6 arthrodesis.The patient sample consisted of 6 patients with single-level (C5-C6) anterior arthrodesis (average age: 48.8±6.9 years; 1 male, 5 female; 7.6±1.2 months postsurgery) and 18 asymptomatic control subjects of similar age (average age: 45.6±5.8 years; 5 male, 13 female).Outcome measures included the physiologic measure of continuous kinematic motion paths at each cervical motion segment (C2-C7) during flexion-extension.Participants performed flexion-extension while biplane radiographs were collected at 30 images per second. A previously validated tracking process determined three-dimensional vertebral positions with submillimeter accuracy. Continuous flexion-extension rotation and anterior-posterior translation motion paths were adjusted for disc height and static orientation of each corresponding motion segment.Intersubject variability in flexion-extension angle was decreased 15% to 46% and intersubject variability in anterior-posterior translation was reduced 14% to 33% after adjusting for disc height and static orientation angle. Average intersubject variability in continuous motion paths was 1.9° in flexion-extension and 0.6 mm in translation. Third-order polynomial equations were determined to precisely describe the continuous flexion-extension and anterior-posterior translation motion path at each motion segment (all R2>0.99).A significant portion of the intersubject variability in cervical kinematics can be explained by the disc height and the static orientation of each motion segment. Clinically relevant information may be gained by assessing intervertebral kinematics during continuous functional movement rather than at static, end range of motion positions. The fidelity of in vitro cervical spine mechanical testing protocols may be evaluated by comparing in vitro kinematics to the continuous motion paths presented.

Project description:Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

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The Influence of Kinematic Constraints on Model Performance During Inverse Kinematics Analysis of the Thoracolumbar Spine.

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