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Quantifying angular joint kinematics of the upper body is a useful method for assessing upper limb function. Joint angles are commonly obtained via motion capture, tracking markers placed on anatomical landmarks. This method is associated with limitations including administrative burden, soft tissue artifacts, and intra- and inter-tester variability. An alternative method involves the tracking of rigid marker clusters affixed to body segments, calibrated relative to anatomical landmarks or known joint angles. The accuracy and reliability of applying this cluster method to the upper body has, however, not been comprehensively explored. Our objective was to compare three different upper body cluster models with an anatomical model, with respect to joint angles and reliability. Non-disabled participants performed two standardized functional upper limb tasks with anatomical and cluster markers applied concurrently. Joint angle curves obtained via the marker clusters with three different calibration methods were compared to those from an anatomical model, and between-session reliability was assessed for all models. The cluster models produced joint angle curves which were comparable to and highly correlated with those from the anatomical model, but exhibited notable offsets and differences in sensitivity for some degrees of freedom. Between-session reliability was comparable between all models, and good for most degrees of freedom. Overall, the cluster models produced reliable joint angles that, however, cannot be used interchangeably with anatomical model outputs to calculate kinematic metrics. Cluster models appear to be an adequate, and possibly advantageous alternative to anatomical models when the objective is to assess trends in movement behavior.

Accuracy of shoulder kinematics predicted by multi-body kinematics optimisation depend on the joint models used. This study assesses the influence of four different subject-specific gleno-humeral joint models within multi-body kinematics optimisation: a 6-degree-of-freedom joint (i.e. single-body kinematics optimisation), a sphere-on-sphere joint (with two spheres of different radii) and a spherical joint with or without penalised translation. To drive these models, the 3D coordinates of 12 skin markers of 6 subjects performing static arm abduction poses up to 180° were used. The reference data was obtained using biplane X-rays from which 3D bone reconstructions were generated: scapula and humerus were 3D reconstructed by fitting a template model made of geometrical primitives on the two bones’ X-rays. Without any motion capture system, the recording of the skin markers was performed at the very same time than the X-rays with radiopaque markers. The gleno-humeral displacements and angles, and scapula-thoracic angles were computed. The gleno-humeral sphere-on-sphere joint provided slightly better results than the spherical joint with or without penalised translation, but considerably better gleno-humeral displacements than the 6-DoF joint. Considering that it can easily be personalised from medical images, this sphere-on-sphere model seems promising for shoulder multi-body kinematics optimisation.

The purpose of this study was to understand how each calibration pose affects scapular orientations measured by an Acromion Marker Cluster during scapular plane arm elevation performed by patients who had been pre-operatively indicated for Reverse Total Shoulder Arthroplasty. Eight pre-operative Reverse Total Shoulder Arthroplasty patients participated in this study while optical motion capture measured kinematics, specifically scapulothoracic angles and angular displacements, vs. humerothoracic elevation. The angle measurements were compared across the static calibration poses used to calculate them within-patient with One Dimensional Statistical Parametric Mapping paired t-tests and across-patients with a series of Sign Tests. The study uncovered patient-specificity in the effects of the Acromion Marker Cluster calibration pose on scapulothoracic angles and near linear offsets between the scapulothoracic upward rotation angles. The scapulothoracic upward rotation angular displacement measurements across calibration poses were within 5° of each other, suggesting nearly linear offsets between upward rotation angle measurements from each calibration pose. The Sign Tests revealed that using the Neutral calibration pose estimated significantly greater scapulothoracic protraction angles during arm elevation than did using the Hand to Back Pocket calibration pose (p = 0.02). Scapulothoracic protraction and posterior tilt measurements were near linear offsets between calibration poses only when humerothoracic elevation was less than 50°. Results encourage patient-specific and humerothoracic elevation-specific methods to combine calibration poses and the development of standards to report scapulothoracic orientations derived from using an Acromion Marker Cluster with multiple calibration poses.

Skin marker-based motion analysis has been widely used to evaluate the functional performance of canine gait and posture. However, the interference of soft tissues between markers and the underlying bones (soft tissue artefacts, STAs) may lead to errors in kinematics measurements. Currently, no optimal marker attachment sites and cluster compositions are recommended for canine gait analysis. The current study aims to evaluate cluster-level STAs and the effects of cluster compositions on the computed stifle kinematics. Ten mixed-breed healthy dogs affixed with 19 retroreflective markers on the thigh and shank were enrolled. During isolated stifle passive extension, the marker trajectories were acquired with a motion capture system, and the skeletal poses were determined by integrating fluoroscopic and CT images of the bones. The cluster-level STAs were assessed, and clusters were paired to calculate the stifle kinematics. A selection of cluster compositions was useful for deriving accurate sagittal and frontal plane stifle kinematics with flexion angles below 50 per cent of the range of motion. The findings contribute to improved knowledge of canine STAs and their influence on motion measurements. The marker composition with the smallest error in describing joint kinematics is recommended for future applications and study in dogs during dynamic gait assessment.

Multi-segment foot models (MSFM) are used in gait analysis for the diagnosis and planning of treatment for patients with foot deformities. Like other biomechanical models, MSFMs represent the leg and foot as a series of linked rigid segments, but such a simplification may not be appropriate, particularly for the flexible forefoot. This study investigated the appropriateness of the rigid body assumption on marker clusters used to define the individual segments (tibia, hindfoot, forefoot) of a widely-used MSFM. Rigidity of the marker clusters was quantified using the rigid body error (σRBE) calculated for each frame of a representative gait cycle for 64 normal healthy adults who underwent gait analysis. σRBE is a measure of how well the tracking marker configuration at each frame compares to the arrangement of the same markers in a reference pose. As expected, the patterns of deformation of the three marker clusters differed over the gait cycle. The hindfoot cluster remained relatively undeformed in comparison to the forefoot and tibia clusters. The largest deformations of the forefoot cluster occurred near the beginning and end of the stance phase. The tibia cluster deformed throughout the entire gait cycle, with a pattern similar to that of a typical knee flexion angle graph. The results raise questions about the appropriateness of the rigid-body assumption when applied to MSFMs, particularly in the forefoot region.

While reconstructing skeletal movement using stereophotogrammetry, the relative movement between a skin marker and the underlying bone is regarded as an artefact (soft tissue artefact: STA). Similarly, the consequent pose, size and shape variations that affect a cluster of markers associated with a bony segment, or any arbitrary change of configuration in the marker local positions as representative of the skin envelope shape variation, may also be looked upon as an STA. Bone pose estimators able to compensate for these artefacts must embed relevant a priori knowledge in the form of an STA mathematical model. Prior to tackling this modeling exercise, an appropriate definition and mathematical representation of the STA time histories must be accomplished. Relevant appropriateness is based on the degree of approximation of the STA reconstruction and on the number of parameters involved. The objective of this study was to propose a generalized mathematical representation of the STA which would be applicable for most plausible definitions of it. To this purpose, a modal approach was used that, most importantly, allows for the splitting of a given STA into additive components (modes). For each STA definition, these modes may be ranked according to the contribution that each of them gives to the reconstruction of the STA. In this way, the STA definition leading to the minimum number of modes, and, therefore, of parameters, that provides an adequate approximation for further purposes can be selected, allowing a trade-off between complexity and effectiveness of the STA model. Using information available in the literature and data provided by an ex-vivo experiment, it is shown that the modes corresponding to the different STA definitions (individual marker displacements, marker-cluster geometrical transformations, and skin envelope shape variations) can be ranked and selected leading, respectively, to a large, moderate or low number of parameters embedded in the STA mathematical representation.

The so-called soft tissue artefacts and wobbling masses have both been widely studied in biomechanics, however most of the time separately, from either a kinematics or a dynamics point of view. As such, the estimation of the stiffness of the springs connecting the wobbling masses to the rigid-body model of the lower limb, based on the in vivo displacements of the skin relative to the underling bone, has not been performed yet. For this estimation, the displacements of the skin markers in the bone-embedded coordinate systems are viewed as a proxy for the wobbling mass movement. The present study applied a structural vibration analysis method called smooth orthogonal decomposition to estimate this stiffness from retrospective simultaneous measurements of skin and intra-cortical pin markers during running, walking, cutting and hopping. For the translations about the three axes of the bone-embedded coordinate systems, the estimated stiffness coefficients (i.e. between 2.3kN/m and 55.5kN/m) as well as the corresponding forces representing the connection between bone and skin (i.e. up to 400N) and corresponding frequencies (i.e. in the band 10–30Hz) were in agreement with the literature. Consistently with the STA descriptions, the estimated stiffness coefficients were found subject- and task-specific.

Accurate estimation of the position and orientation (pose) of a bone from a cluster of skin markers is limited mostly by the relative motion between the bone and the markers, which is known as the soft tissue artifact (STA). This work presents a method, based on continuum mechanics, to describe the kinematics of a cluster affected by STA. The cluster is characterized by triangular cosserat point elements (TCPEs) defined by all combinations of three markers. The effects of the STA on the TCPEs are quantified using three parameters describing the strain in each TCPE and the relative rotation and translation between TCPEs. The method was evaluated using previously collected ex vivo kinematic data. Femur pose was estimated from 12 skin markers on the thigh, while its reference pose was measured using bone pins. Analysis revealed that instantaneous subsets of TCPEs exist which estimate bone position and orientation more accurately than the Procrustes Superimposition applied to the cluster of all markers. It has been shown that some of these parameters correlate well with femur pose errors, which suggests that they can be used to select, at each instant, subsets of TCPEs leading an improved estimation of the underlying bone pose.

In flexion–extension motion, the interaction of several ligaments and bones characterizes the elbow joint stability. The aim of this preliminary study was to quantify the relative motion of the ulna with respect to the humerus in two human upper limbs specimens and to investigate the constraints role for maintaining the elbow joint stability in different section conditions. Two clusters of four markers were fixed respectively to the ulna and humerus, and their trajectory was recorded by a motion capture system during functional orthopedic maneuver. Considering the posterior bundle of medial collateral complex (pMUCL) and the coronoid, two section sequences were executed. The orthopedic maneuver of compression, pronation and varus force was repeated at 30°, 60° and 90° flexion for the functional investigation of constraints. Ulna deflection was compared to a baseline elbow flexion condition. With respect to the intact elbow, the coronoid osteotomy influences the elbow stability at 90° (deflection = 11.49 ± 17.39 mm), while small differences occur at 30° and 60°, due to ligaments constraint. The contemporary pMUCL section and coronoid osteotomy causes elbow instability, with large deflection at 30° (deflection = 34.40 ± 9.10 mm), 60° (deflection = 45.41 ± 18.47 mm) and 90° (deflection = 52.16 ± 21.92 mm). Surgeons may consider the pMUCL reconstruction in case of unfixable coronoid fracture.

Lack of complexity in general movements in early infancy is an important marker of potential motor disorders of neurological origin, such as cerebral palsy. Quantitative approaches to characterising this complexity are hampered by experimental difficulties in recording from infants in their first few months of life. The aim of this study was to design and validate bespoke surface-marker clusters to facilitate data acquisition and enable full quantification of joint rotations. The clusters were validated by recording the controlled movements of a soft-body dummy doll simultaneously with an optical (Qualisys) and inertial (XSens) motion capture system. The angles estimated from the optical system were compared with those measured by the inertial system. We demonstrate that the surface-marker based approach compares well with the use of an inertial system to obtain “direct” readings of the rotations whilst alleviating the issues associated with the use of an optical motion capture system. We briefly report use of this technique in 1–5 month old infants. By enabling full quantification of joint rotation, use of the custom made markers could pave the way for early diagnosis of movement disorders.