A hip joint kinematics driven model for the generation of realistic thigh soft tissue artefacts

Can Anthropometry be Used to Dictate Participant-Specific Thigh Marker Placements Which Minimize Error in Hip Joint Center Estimation?

Specific participant characteristics may be leveraged to dictate marker placements which reduce soft tissue artifact; however, a better understanding of the relationships between participant characteristics and soft tissue artifact are first required. The purpose of this study was to assess the accuracy in which measures of whole-body and thigh anthropometry could predict mislocation error of the hip joint center, tracked using skin-mounted marker clusters. Fifty participants completed squatting and kneeling, while pelvis and lower limb motion were recorded. The effect of soft tissue artifact was estimated from 6 rigid thigh marker clusters by evaluating their ability to track the position of the hip joint center most like the pelvis cluster. Eighteen backward stepwise linear regressions were performed using 10 anthropometric measures as independent variables and the mean of the peak difference between the thigh and pelvis cluster-tracked hip joint centers. Fourteen models significantly predicted error with low to moderate fit (R = .38-.67), explaining 14% to 45% of variation. Partial correlations indicated that soft tissue artifact may increase with soft tissue volume and be altered by local soft tissue composition. However, it is not recommended that marker placement be adjusted based on anthropometry alone.

A joint kinematics driven model of the pelvic soft tissue artefact

When skin-markers trajectories are used in human movement analysis, compensating for their relative movement with respect to the underlying bone (soft tissue artefact, STA) is essential for accurate bone-pose estimation; information about the artefact is required in the form of a mathematical model. Such model, not available for pelvic artefacts, could allow pelvic STA compensation in routine gait analysis by embedding it in skeletal kinematics estimators and developing ad-hoc optimization problems for the estimate of subject-specific model parameters. It was developed as driven by adjacent body segment kinematics. Model architecture feasibility was tested; its compensation effectiveness was assessed evaluating the error in pelvic orientation after removing the modelled artefact from the measured one. Five volunteers with a wide body mass range (BMI: 22-37) underwent MRI scans to reconstruct subject-specific pelvic digital bone models. Multiple anatomical calibrations performed in different static postures, as occurring during walking and star-arc movements, registering the bone-models with points digitized through stereophotogrammetry over pelvic bony prominences, allowed to define the relevant poses of a pelvis-embedded anatomical coordinate system. Such approach allowed to measure STAs over several pelvic anatomical landmarks, for each posture and subject. Model parameters were estimated by minimizing the least squares difference between measured and modelled STAs. The measured STAs were appropriately modelled with subject-specific calibrations, both in terms of shape (correlation coefficient: median [inter-quartile-range]: 0.72 [0.36]) and amplitude (root mean square residual: 3.0 [3.2] mm). Consequently, the overall error in pelvic orientation vector (5.1 [4.4] deg) was reduced after removing the modelled artefacts (2.5 [1.9] deg).

Joint Center Estimation Using Single-Frame Optimization: Part 2: Experimentation

Human motion capture is driven by joint center location estimates, and error in their estimation can be compounded by subsequent kinematic calculations. Soft tissue artifact (STA), the motion of tissue relative to the underlying bones, is a primary cause of error in joint center calculations. A method for mitigating the effects of STA, single-frame optimization (SFO), was introduced and numerically verified in Part 1 of this work, and the purpose of this article (Part 2) is to experimentally compare the results of SFO with a marker-based solution. The experimentation herein employed a single-degree-of-freedom pendulum to simulate human joint motion, and the effects of STA were simulated by affixing the inertial measurement unit to the pendulum indirectly through raw, vacuum-sealed meat. The inertial sensor was outfitted with an optical marker adapter so that its location could be optically determined by a camera-based motion-capture system. During the motion, inertial effects and non-rigid attachment of the inertial sensor caused the simulated STA to manifest via unrestricted motion (six degrees of freedom) relative to the rigid pendulum. The redundant inertial and optical instrumentation allowed a time-varying joint center solution to be determined both by optical markers and by SFO, allowing for comparison. The experimental results suggest that SFO can achieve accuracy comparable to that of state-of-the-art joint center determination methods that use optical skin markers (root mean square error of 7.87–37.86 mm), and that the time variances of the SFO solutions are correlated (r =  0.58–0.99) with the true, time-varying joint center solutions. This suggests that SFO could potentially help to fill a gap in the existing literature by improving the characterization and mitigation of STA in human motion capture.

Effect of tibia marker placement on knee joint kinematic analysis

Variability of kinematic measures determined by different marker sets among sites participating in a collaborative study is necessary for determining the reliability of a multi-site gait analysis research. We compared knee kinematics based on different marker sets on the tibia, calculating by segmental optimization (SO) and multi-body optimization (MBO) methods respectively, in order to assess the effect of marker locations on the methods. 11 healthy subjects participated in the study with 33 markers attached to the lower extremity segments, and 4 groups were identified according to markers on the tibia. Knee joint kinematics during level walking were measured and then compared among the 4 groups using statistical parametric mapping. For SO method, the results showed that there were no significant differences in the knee joint angles when used different marker sets on the tibia. However, significant differences were found in the transverse plane kinematics for MBO method. It was concluded that MBO method was more likely to be influenced by different marker sets. More attention should be paid to marker sets, specifically for MBO method, when three-dimensional gait analysis data are shared and interpreted among sites for clinical decision-making.

Standardization proposal of soft tissue artefact description for data sharing in human motion measurements

Soft tissue artefact (STA) represents one of the main obstacles for obtaining accurate and reliable skeletal kinematics from motion capture. Many studies have addressed this issue, yet there is no consensus on the best available bone pose estimator and the expected errors associated with relevant results. Furthermore, results obtained by different authors are difficult to compare due to the high variability and specificity of the phenomenon and the different metrics used to represent these data. Therefore, the aim of this study was twofold: firstly, to propose standards for description of STA; and secondly, to provide illustrative STA data samples for body segments in the upper and lower extremities and for a range of motor tasks specifically, level walking, stair ascent, sit-to-stand, hip- and knee-joint functional movements, cutting motion, running, hopping, arm elevation and functional upper-limb movements. The STA dataset includes motion of the skin markers measured in vivo and ex vivo using stereophotogrammetry as well as motion of the underlying bones measured using invasive or bio-imaging techniques (i.e., X-ray fluoroscopy or MRI). The data are accompanied by a detailed description of the methods used for their acquisition, with information given about their quality as well as characterization of the STA using the proposed standards. The availability of open-access and standard-format STA data will be useful for the evaluation and development of bone pose estimators thus contributing to the advancement of three-dimensional human movement analysis and its translation into the clinical practice and other applications.

Effects of soft tissue artifacts on differentiating kinematic differences between natural and replaced knee joints during functional activity

Functional performance of total knee replacement (TKR) is often assessed using skin marker-based stereophotogrammetry, which can be affected by soft tissue artifacts (STA). The current study aimed to compare the STA and their effects on the kinematics of the knee between twelve patients with TKR and twelve healthy controls during sit-to-stand, and to assess the effects of STA on the statistical between-group comparisons. Each subject performed the sit-to-stand task while motions of the skin markers and the knees were measured by a motion capture system integrated with a three-dimensional fluoroscopy technique. The bone motions measured by the three-dimensional fluoroscopy were taken as the gold standard, with respect to which the STA of the markers were obtained. The STA were found to affect the calculated segmental poses and knee kinematics between the groups differently. The STA resulted in artefactual posterior displacements of the knee joint center, with magnitudes significantly greater in TKR than controls (p<0.01). The STA-induced knee external rotations in TKR were smaller than those in controls with mean differences of 2.3-3.0°. These between-group differences in the STA effects on knee kinematics in turn concealed the true between-group differences in the anterior-posterior translation and internal/external rotation of knee while leading to false significant between-group differences in the abduction/adduction and proximal-distal translation.

Side-to-side variation in normal femoral morphology: 3D CT analysis of 122 femurs

BACKGROUND

The contralateral femur is often used as reference for reconstruction in unilateral hip joint pathology. The objective of this study was to quantify the side-to-side variation in proximal femur. We hypothesized that significant side-to-side differences exist between left and right femur with implications for preoperative planning and leg length discrepancy following hip arthroplasty.

MATERIALS AND METHODS

CT-based 3D femoral models were reconstructed for 122 paired femurs in 61 young healthy subjects (46.9±6.8 years) with no history of hip pathology. Side-to-side differences of several femoral morphologic parameters, including femoral head diameter, femoral anteversion, horizontal offset and femoral head center location, were compared and correlated with demographic factors using multiple linear regression.

RESULTS

Significant side-to-side differences (P<0.01) were found in femoral anteversion (4.3±3.8°; range: 0.2° to 17.3°), horizontal offset (2.5±2.1mm; range: 0.1 to 10.3mm), and femoral head center location (7.1±3.8mm; range: 0.5 to 19.4mm). The difference in femoral anteversion was strongly correlated with the difference in neck diameter (R(2)=0.79), whereas the difference in horizontal femoral offset was highly correlated with the head diameter difference (R(2)=0.72). Femoral head center difference was correlated with the femoral anteversion, horizontal offset and neck-shaft-angle difference (R(2)=0.82).

DISCUSSION

Relying on the anatomic landmarks of the contralateral femur during hip arthroplasty may not necessarily result in restoration of native anatomy and leg-length. Knowledge of the baseline side-to-side asymmetry could provide a range of error that would be tolerable following hip reconstruction.

LEVEL OF EVIDENCE

Level IV.

TYPE OF STUDY

Retrospective observational study.

Rigid and non-rigid geometrical transformations of a marker-cluster and their impact on bone-pose estimation

When stereophotogrammetry and skin-markers are used, bone-pose estimation is jeopardised by the soft tissue artefact (STA). At marker-cluster level, this can be represented using a modal series of rigid (RT; translation and rotation) and non-rigid (NRT; homothety and scaling) geometrical transformations. The NRT has been found to be smaller than the RT and claimed to have a limited impact on bone-pose estimation. This study aims to investigate this matter and comparatively assessing the propagation of both STA components to bone-pose estimate, using different numbers of markers. Twelve skin-markers distributed over the anterior aspect of a thigh were considered and STA time functions were generated for each of them, as plausibly occurs during walking, using an ad hoc model and represented through the geometrical transformations. Using marker-clusters made of four to 12 markers affected by these STAs, and a Procrustes superimposition approach, bone-pose and the relevant accuracy were estimated. This was done also for a selected four marker-cluster affected by STAs randomly simulated by modifying the original STA NRT component, so that its energy fell in the range 30-90% of total STA energy. The pose error, which slightly decreased while increasing the number of markers in the marker-cluster, was independent from the NRT amplitude, and was always null when the RT component was removed. It was thus demonstrated that only the RT component impacts pose estimation accuracy and should thus be accounted for when designing algorithms aimed at compensating for STA.

A model of the soft tissue artefact rigid component

When using stereophotogrammetry and skin-markers, the reconstruction of skeletal movement is affected by soft-tissue artefact (STA). This may be described by considering a marker-cluster as a deformable shape undergoing a geometric transformation formed by a non-rigid (change in size and shape) and a rigid component (translation and rotation displacements). A modal decomposition of the STA, relative to an appropriately identified basis, allows the separation of these components. This study proposes a mathematical model of the STA that embeds only its rigid component and estimates the relevant six mode amplitudes as linear functions of selected proximal and distal joint rotations during the analysed task. This model was successfully calibrated for thigh and shank using simultaneously recorded pin- and skin-marker data of running volunteers. The root mean square difference between measured and model-estimated STA rigid component was 1.1(0.8)mm (median (inter-quartile range) over 3 subjects × 5 trials × 33 markers coordinates), and it was mostly due to the wobbling not included in the model. Knee joint kinematics was estimated using reference pin-marker data and skin-marker data, both raw and compensated with the model-estimated STA. STA compensation decreased inaccuracy on average from 6% to 1% for flexion/extension, from 43% to 18% for the other two rotations, and from 69% to 25% for the linear displacements. Thus, the proposed mathematical model provides an STA estimate which can be effectively used within optimal bone pose and joint kinematics estimators for artefact compensation, and for simulations aimed at their comparative assessments.

What portion of the soft tissue artefact requires compensation when estimating joint kinematics?

When joint kinematics is analyzed using noninvasive stereophotogrammetry, movements of the skin markers relative to the underlying bone are regarded as artefacts (soft tissue artefact (STA)). Recent literature suggests that an appropriate estimation of joint kinematics may be obtained by compensating for only a portion of the STA, but no evidence for this case has been reported, and which portion of the STA should be selected remains an issue. The aim of this study was to fill this gap. A modal approach was used to represent the STA. This resulted in a series of additive components (modes) and in the possibility to select a subset of them. The following STA definitions were used: individual skin marker displacement (MD), marker-cluster geometrical transformation (GT), and skin envelope shape variation (SV). An STA approximation for each of the three definitions was obtained by ordering modes on the basis of their contribution to the total STA energy and truncating the relevant series at 90% of it. A fourth approximation was obtained when the GT definition was used, by selecting the modes that represented the marker-cluster rigid transformation (i.e., three translation and three rotation modes). The different STA approximations were compared using data obtained during the stance phase of running of three volunteers carrying both pin and skin markers. The STA was measured and knee joint kinematics estimated using four skin marker datasets compensated for the above-mentioned STA approximations. Accuracy was assessed by comparing results to the reference kinematics obtained using pin markers. The different approximations resulted in different numbers of modes. For joint angles, the compensation efficiency across the STA approximations based on an energy threshold was almost equivalent. The median root mean square errors (RMSEs) were below 1 deg for flexion/extension and 2 deg for both abduction/adduction and internal/external rotation. For the joint displacements, the compensation efficiency depended on the STA approximation. Median RMSEs for anterior/posterior displacement ranged from 1 to 4 mm using either MD, GT, or SV truncated series. The RMSEs were virtually null when the STA was approximated using only the GT rigid modes. This result, together with the limited number of modes used by this approximation (i.e., three translations and three rotations of the marker-cluster), makes the STA rigid component and a good candidate for designing an STA model to be incorporated in an enhanced bone pose estimator.

Soft tissue artifact distribution on lower limbs during treadmill gait: Influence of skin markers' location on cluster design

Segment poses and joint kinematics estimated from skin markers are highly affected by soft tissue artifact (STA) and its rigid motion component (STARM). While four marker-clusters could decrease the STA non-rigid motion during gait activity, other data, such as marker location or STARM patterns, would be crucial to compensate for STA in clinical gait analysis. The present study proposed 1) to devise a comprehensive average map illustrating the spatial distribution of STA for the lower limb during treadmill gait and 2) to analyze STARM from four marker-clusters assigned to areas extracted from spatial distribution. All experiments were realized using a stereophotogrammetric system to track the skin markers and a bi-plane fluoroscopic system to track the knee prosthesis. Computation of the spatial distribution of STA was realized on 19 subjects using 80 markers apposed on the lower limb. Three different areas were extracted from the distribution map of the thigh. The marker displacement reached a maximum of 24.9 mm and 15.3 mm in the proximal areas of thigh and shank, respectively. STARM was larger on thigh than the shank with RMS error in cluster orientations between 1.2° and 8.1°. The translation RMS errors were also large (3.0 mm to 16.2 mm). No marker-cluster correctly compensated for STARM. However, the coefficient of multiple correlations exhibited excellent scores between skin and bone kinematics, as well as for STARM between subjects. These correlations highlight dependencies between STARM and the kinematic components. This study provides new insights for modeling STARM for gait activity.

Can optimal marker weightings improve thoracohumeral kinematics accuracy?

Local and global optimization algorithms have been developed to estimate joint kinematics to reducing soft movement artifact (STA). Such algorithms can include weightings to account for different STA occur at each marker. The objective was to quantify the benefit of optimal weighting and determine if optimal marker weightings can improve humerus kinematics accuracy. A pin with five reflective markers was inserted into the humerus of four subjects. Seven markers were put on the skin of the arm. Subjects performed 38 different tasks including arm elevation, rotation, daily-living tasks, and sport activities. In each movement, mean and peak errors in skin- vs. pins-orientation were reported. Then, optimal marker weightings were found to best match skin- and pin-based orientation. Without weighting, the error of the arm orientation ranged from 1.9° to 17.9°. With weighting, 100% of the trials were improved and the average error was halved. The mid-arm markers weights were close to 0 for three subjects. Weights of a subject applied to the others for a given movement, and weights of a movement applied to others for a given subject did not systematically increased accuracy of arm orientation. Without weighting, a redundant set of marker and least square algorithm improved accuracy to estimate arm orientation compared to data of the literature using electromagnetic sensor. Weightings were subject- and movement-specific, which reinforces that STA are subject- and movement-specific. However, markers on the deltoid insertion and on lateral and medial epicondyles may be preferred if a limited number of markers is used.

Hip joint centre position estimation using a dual unscented Kalman filter for computer-assisted orthopaedic surgery

In computer-assisted knee surgery, the accuracy of the localization of the femur centre of rotation relative to the hip-bone (hip joint centre) is affected by the unavoidable and untracked pelvic movements because only the femoral pose is acquired during passive pivoting manoeuvres. We present a dual unscented Kalman filter algorithm that allows the estimation of the hip joint centre also using as input the position of a pelvic reference point that can be acquired with a skin marker placed on the hip, without increasing the invasiveness of the surgical procedure. A comparative assessment of the algorithm was carried out using data provided by in vitro experiments mimicking in vivo surgical conditions. Soft tissue artefacts were simulated and superimposed onto the position of a pelvic landmark. Femoral pivoting made of a sequence of star-like quasi-planar movements followed by a circumduction was performed. The dual unscented Kalman filter method proved to be less sensitive to pelvic displacements, which were shown to be larger during the manoeuvres in which the femur was more adducted. Comparable accuracy between all the analysed methods resulted for hip joint centre displacements smaller than 1 mm (error: 2.2 ± [0.2; 0.3] mm, median ± [inter-quartile range 25%; inter-quartile range 75%]) and between 1 and 6 mm (error: 4.8 ± [0.5; 0.8] mm) during planar movements. When the hip joint centre displacement exceeded 6 mm, the dual unscented Kalman filter proved to be more accurate than the other methods by 30% during multi-planar movements (error: 5.2 ± [1.2; 1] mm).

A soft tissue artefact model driven by proximal and distal joint kinematics

When analysing human movement through stereophotogrammetry, skin-markers are used. Their movement relative to the underlying bone is known as a soft tissue artefact (STA). A mathematical model to estimate subject- and marker-specific STAs generated during a given motor task, is required for both skeletal kinematic estimators and comparative assessment using simulation. This study devises and assesses such a mathematical model using the paradigmatic case of thigh STAs. The model was based on two hypotheses: (1) that the artefact mostly depends on skin sliding, and thus on the angles of hip and knee; (2) that the relevant relationship is linear. These hypotheses were tested using data obtained from passive hip and knee movements in non-obese specimens and from running volunteers endowed with both skin- and pin-markers. Results showed that the proposed model could be calibrated with small residuals and that the thigh artefacts were mostly due to skin sliding, not only ex-vivo, as expected, but also in-vivo. This was corroborated by the observation that in-vivo, the portion of the artefact not reconstructed by the model fell within a frequency band compatible with soft tissue wobbling and carried a relatively small portion of total mean power (13%, on average). Thus, the architecture of our model is feasible both ex-vivo and in-vivo and can, in principle, be used in skeletal kinematics estimators. The generalizability of a calibrated model across different movements was proved doable, albeit limited to movement patterns similar to those of the calibration movement, even if joint rotation ranges can be remarkably different. Therefore, such a calibrated model can be used for generating realistic STAs for simulation purposes.

Propagation of soft tissue artifacts to the center of rotation: a model for the correction of functional calibration techniques

This paper presents a mathematical model for the propagation of errors in body segment kinematics to the location of the center of rotation. Three functional calibration techniques, usually employed for the gleno-humeral joint, are studied: the methods based on the pivot of the instantaneous helical axis (PIHA) or the finite helical axis (PFHA), and the "symmetrical center of rotation estimation" (SCoRE). A procedure for correcting the effect of soft tissue artifacts is also proposed, based on the equations of those techniques and a model of the artifact, like the one that can be obtained by double calibration. An experiment with a mechanical analog was performed to validate the procedure and compare the performance of each technique. The raw error (between 57 and 68mm) was reduced by a proportion of between 1:6 and less than 1:15, depending on the artifact model and the mathematical method. The best corrections were obtained by the SCoRE method. Some recommendations about the experimental setup for functional calibration techniques and the choice of a mathematical method are derived from theoretical considerations about the formulas and the results of the experiment.