Generalized mathematical representation of the soft tissue artefact

Quantification of soft tissue artifacts using CT registration and subject-specific multibody modeling

The potential use of gait analysis for quantitative preoperative planning in total hip arthroplasty (THA) has previously been demonstrated. However, the joint kinematic data measured through this process tend to be unreliable for surgical planning due to distortions caused by soft tissue artifacts (STAs). In this study, we developed a novel motion capture framework by combining computed tomography (CT)-based postural calibration and subject-specific multibody dynamics modeling to prevent the effect of STAs in measuring hip kinematics. Three subjects with femoroacetabular impingement syndrome were recruited, and CT data for each patient were collected by attaching marker clusters near the hip. A subject-specific multibody hip joint model was developed based on reconstructed CT data. Spring-dashpot network calculations were performed to minimize the distance between the anatomical landmark and its corresponding infrared reflective marker. The STAs of the thigh was described as six degrees of freedom viscoelastic bushing elements, and their parameter values were identified via smooth orthogonal decomposition. Least squares optimization was used to modify the pelvic rotations to compensate for the rigid components of STAs. The results showed that CT-assisted motion tracking enabled the successful identification of STA influences in gait and squat positions. Furthermore, STA effects were found to alter maximal pelvis tilt and hip rotations during a squat. Compared to other techniques, such as dual fluoroscopic imaging, the adopted framework does not require additional medical imaging for patients undergoing robot-assisted THA surgery and is thus a practical way of evaluating hip joint kinematics for preoperative surgical planning.

Assessment of Stability of MIMU Probes to Skin-Marker-Based Anatomical Reference Frames During Locomotion Tasks: Effect of Different Locations on the Lower Limb

Soft tissue artefacts (STAs) undermine the validity of skin-mounted approaches to measure skeletal kinematics. Magneto-inertial measurement units (MIMU) gained popularity due to their low cost and ease of use. Although the reliability of different protocols for marker-based joint kinematics estimation has been widely reported, there are still no indications on where to place MIMU to minimize STA. This study aims to find the most stable positions for MIMU placement, among four positions on the thigh, four on the shank, and three on the foot. Stability was investigated by measuring MIMU movements against an anatomical reference frame, defined according to a standard marker-based approach. To this aim, markers were attached both on the case of each MIMU (technical frame) and on bony landmarks (anatomical frame). For each MIMU, the nine angles between each versor of the technical frame with each versor of the corresponding anatomical frame were computed. The maximum standard deviation of these angles was assumed as the instability index of MIMU-body coupling. Six healthy subjects were asked to perform barefoot gait, step negotiation, and sit-to-stand. Results showed that (1) in the thigh, the frontal position was the most stable in all tasks, especially in gait; (2) in the shank, the proximal position is the least stable, (3) lateral or medial calcaneus and foot dorsum positions showed equivalent stability performances. Further studies should be done before generalizing these conclusions to different motor tasks and MIMU-body fixation methods. The above results are of interest for both MIMU-based gait analysis and rehabilitation approaches using wearable sensors-based biofeedback.

Quantification of the errors associated with marker occlusion in stereophotogrammetric systems and implications on gait analysis

Optoelectronic stereophotogrammetric systems (OSSs) represent the standard for gait analysis. Despite widespread, their reported accuracy in nominal working conditions shows a variability of several orders of magnitude, ranging from few microns to several millimetres. No clear explanation for this variability has been provided yet. We hypothesized that this reflects an error affecting OSS outcomes when some of the tracked markers are totally or partially occluded. The aim of this paper is to quantify this error in static and dynamic conditions, also distinguishing between total and partial marker occlusion. A Vicon system featuring 8 cameras is employed in this study. Two camera distributions, one designed to maximize OSS accuracy and another one representative of a typical gait setup, are investigated. For both the setups, static and dynamic tests are performed, evaluating the different impact of partial and total marker occlusions. Marker occlusions significantly affected the system performances. The maximum measure variation reached 1.86 mm and 7.20 mm in static and dynamic conditions, respectively, both obtained in the case of partial occlusion. This systematic source of error is likely to affect gait measures: markers placed on the patient body are often visible only by half of the cameras, with swinging arms and legs providing moving occlusions. The maximum error observed in this study can potentially affect the kinematics outcomes of conventional gait models, particularly on frontal and coronal plane, and consequently the peak muscle forces estimated with musculoskeletal models.

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).

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.

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.

Assessment of Knee Cartilage Stress Distribution and Deformation Using Motion Capture System and Wearable Sensors for Force Ratio Detection

Knowledge about the knee cartilage deformation ratio as well as the knee cartilage stress distribution is of particular importance in clinical studies due to the fact that these represent some of the basic indicators of cartilage state and that they also provide information about joint cartilage wear so medical doctors can predict when it is necessary to perform surgery on a patient. In this research, we apply various kinds of sensors such as a system of infrared cameras and reflective markers, three-axis accelerometer, and force plate. The fluorescent marker and accelerometers are placed on the patient's hip, knee, and ankle, respectively. During a normal walk we are recording the space position of markers, acceleration, and ground reaction force by force plate. Measured data are included in the biomechanical model of the knee joint. Geometry for this model is defined from CT images. This model includes the impact of ground reaction forces, contact force between femur and tibia, patient body weight, ligaments, and muscle forces. The boundary conditions are created for the finite element method in order to noninvasively determine the cartilage stress distribution.

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.

Surface marker cluster translation, rotation, scaling and deformation: Their contribution to soft tissue artefact and impact on knee joint kinematics

When recording human movement with stereophotogrammetry, skin deformation and displacement (soft tissue artefact; STA) inhibits surface markers' ability to validly represent the movement of the underlying bone. To resolve this issue, the components of marker motions which contribute to STA must be understood. The purpose of this study is to describe and quantify which components of this marker motion (cluster translation, rotation, scaling and deformation) contribute to STA during the stance phase of walking, a cutting manoeuvre, and one-legged hops. In vivo bone pin-based tibio-femoral kinematics of six healthy subjects were used to study skin marker-based STA. To quantify how total cluster translation, rotation, scaling and deformation contribute to STA, a resizable and deformable cluster model was constructed. STA was found to be greater in the thigh than the shank during all three movements. We found that the non-rigid (i.e. scaling and deformation) movements contribute very little to the overall amount of error, rendering surface marker optimisation methods aimed at minimising this component superfluous. The results of the current study indicate that procedures designed to account for cluster translation and rotation during human movement are required to correctly represent the motion of body segments, however reducing marker cluster scaling and deformation will have little effect on STA.

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.