A novel method intersecting three-dimensional motion capture and medial elbow strength dynamometry to assess elbow injury risk in baseball pitchers

In baseball pitching, resultant elbow varus torque reaches the peak value of 50-120 N m, exceeding the joint failure limit that risks damage to the ulnar collateral ligament (UCL). In-vivo methodology is lacking to assess whether pitchers have sufficient muscular strength to shield UCL and how strongly the elbow musculature must contract to minimize valgus loading on UCL. This study introduces a method to assess relative percentages of muscular varus strength required to unload the UCL. The maximum voluntary isometric varus strength (MVIVS) produced by the medial elbow musculature and the maximum resultant varus torques at elbow in pitching fastballs and other types were measured for two professional pitchers. Simulation was conducted to determine the relative percentages of MVIVS required to unload the UCL to varying degrees and the impact of athletes' previous UCL reconstruction on the relative percentages was examined. The maximum resultant varus torque in pitching was found to range 72-97%MVIVS depending on the type of pitch. The elbow musculature had to produce 21-49%MVIVS to avoid acute failure of intact UCL whereas the corresponding requirements were 39-63%MVIVS for UCL reconstructed joint. The method offers new insight into baseball pitcher's training/rehabilitation and physical assessment to reduce the risk of UCL injury.

Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors

In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint center (S2J) vectors where the S2J vectors are predetermined as constants under the assumption of rigid body segments. However, human body segments are not rigid bodies because they are easily affected by soft tissue artifacts (STAs). Therefore, the use of the constant S2J vectors is one of the most critical factors for the inaccurate estimation of relative position. To deal with this issue, this paper proposes a method of determining time-varying S2J vectors to reflect the deformation of the S2J vectors and thus to increase the estimation accuracy, in IMMU-based relative position estimation. For the proposed method, first, reference S2J vectors for learning needed to be collected. A regression method derived a function outputting S2J vectors based on specific physical quantities that were highly correlated with the deformation of S2J vectors. Subsequently, time-varying S2J vectors were determined from the derived function. The validation results showed that, in terms of the averaged root mean squared errors of four tests performed by three subjects, the proposed method (15.08 mm) provided a higher estimation accuracy than the conventional method using constant vectors (31.32 mm). This indicates the proposed method may effectively compensate for the effects of STAs and ultimately estimate more accurate relative positions. By providing STA-compensated relative positions between segments, the proposed method applied in a wearable motion tracking system can be useful in rehabilitation or sports sciences.

Efficacy of quantifying marker-cluster rigidity in a multi-segment foot model: a Monte-Carlo based global sensitivity analysis and regression model

Marker-based clinical gait analysis and multi-segment foot models (MSFM) have been successfully used for the diagnosis and clinical management of various lower limb disorders. The accuracy and validity of the kinematics measured depend on the design of the model, as well as on the adherence to its inherent rigid body assumption. This study applies a Monte-Carlo based global sensitivity analysis to evaluate the efficacy of using 'rigid body error (σRBE)' in quantifying the rigidity of a MSFM marker-cluster. A regression model is proposed. It is concluded that σRBE is effective in quantifying rigidity.

A patella marker to improve hip and knee kinematics for models with functionally defined joint axes

BACKGROUND

The clinical utility of motion capture modeling relies on the accurate tracking of segment motions. Soft tissue artefact presents a particular challenge for modeling hip rotation, knee rotation, and knee varus-valgus motions. The integration of a patella marker has been shown to significantly improve hip rotation tracking for models that utilize anatomical definitions of joint axes (e.g. anatomical models). However, these modeling improvements have not been extended to models that use functional segment motion to define joint axes (e.g. functional models).

RESEARCH QUESTION

How does the positioning of a patella marker influence functional model performance?

METHODS

A patella functional model (PFM) was created by integrating a patella marker into the functional model (FM) used at our center. Nine distinct versions of the PFM were created using a 3 × 3 grid of markers placed across the patella. Ten typically developing participants performed controlled hip rotation, controlled knee flexion-extension, and free speed walking trials to assess FM and PFM performance differences.

RESULTS

The top performing PFM modeled 98 ± 8 % of the reference hip rotation range of motion compared to 71 ± 9 % for the FM. This PFM had low sensitivity to knee flexion-extension motion, 5 ± 10 %. For walking kinematics, this top performing PFM reported 14 % greater hip rotation ROM during stance, 46 % less knee rotation ROM over the entire gait cycle, and 32 % less knee varus-valgus during swing compared to the FM. The differences in modeling are nearly identical to those reported between skin mounted marker and fluoroscopy-based models, indicating that utilization of the patella marker leads to improvements in tracking accuracy.

SIGNIFICANCE

Utilization of a precisely placed patella marker led to substantial improvements in modeled hip rotation, knee rotation, and knee varus-valgus. These improvements have the potential to positively impact those specialties that rely on motion capture modeling for clinical decision-making, such as orthopedic surgery.

A methodology for the customization of hinged ankle-foot orthoses based on in vivo helical axis calculation with 3D printed rigid shells

This study aims to develop techniques for ankle joint kinematics analysis using motion capture based on stereophotogrammetry. The scope is to design marker attachments on the skin for a most reliable identification of the instantaneous helical axis, to be targeted for the fabrication of customized hinged ankle-foot orthoses. These attachments should limit the effects of the experimental artifacts, in particular the soft-tissue motion artifact, which affect largely the accuracy of any in vivo ankle kinematics analysis. Motion analyses were carried out on two healthy subjects wearing customized rigid shells that were designed through 3D scans of the subjects' lower limbs and fabricated by additive manufacturing. Starting from stereophotogrammetry data collected during walking and dorsi-plantarflexion motor tasks, the instantaneous and mean helical axes of ankle joint were calculated. The customized shells matched accurately the anatomy of the subjects and allowed for the definition of rigid marker clusters that improved the accuracy of in vivo kinematic analyses. The proposed methodology was able to differentiate between subjects and between the motor tasks analyzed. The observed position and dispersion of the axes were consistent with those reported in the literature. This methodology represents an effective tool for supporting the customization of hinged ankle-foot orthoses or other devices interacting with human joints functionality.

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).

Augmented reality with HoloLens in parotid surgery: how to assess and to improve accuracy

PURPOSE

Augmented reality improves planning and execution of surgical procedures. The aim of this study was to evaluate the feasibility of a 3D augmented reality hologram in live parotic surgery. Another goal was to develop an accuracy measuring instrument and to determine the accuracy of the system.

METHODS

We created a software to build and manually align 2D and 3D augmented reality models generated from MRI data onto the patient during surgery using the HoloLens^® 1 (Microsoft Corporation, Redmond, USA). To assess the accuracy of the system, we developed a specific measuring tool applying a standard electromagnetic navigation device (Fiagon GmbH, Hennigsdorf, Germany).

RESULTS

The accuracy of our system was measured during real surgical procedures. Training of the experimenters and the use of fiducial markers significantly reduced the accuracy of holographic system (p = 0.0166 and p = 0.0132). Precision of the developed measuring system was very high with a mean error of the basic system of 1.3 mm. Feedback evaluation demonstrated 86% of participants agreed or strongly agreed that the HoloLens will play a role in surgical education. Furthermore, 80% of participants agreed or strongly agreed that the HoloLens is feasible to be introduced in clinical routine and will play a role within surgery in the future.

CONCLUSION

The use of fiducial markers and repeated training reduces the positional error between the hologram and the real structures. The developed measuring device under the use of the Fiagon navigation system is suitable to measure accuracies of holographic augmented reality images of the HoloLens.

Description of soft tissue artifacts and related consequences on hindlimb kinematics during canine gait

Background

Soft tissue artifacts (STAs) are a source of error in marker-based gait analysis in dogs. While some studies have revealed the existence of STAs in the canine hindlimb, STAs and their influence on kinematic gait analysis remain unclear.

Methods

Thirteen healthy Taiwan dogs affixed with twenty skin markers on the thigh and crus were recruited. Soft tissue artifacts and their influence on the determination of segment poses and stifle angles were assessed by simultaneously measuring marker trajectories and kinematics of the underlying bones via a model-based fluoroscopic analysis method.

Results

Markers on the thigh showed higher STAs than those on the crus, with root-mean-square amplitudes up to 15.5 mm. None of the tested marker clusters were able to accurately reproduce the skeletal poses, in which the maximum root-mean-square deviations ranged from 3.4° to 8.1°. The use of markers resulted in overestimated stifle flexion during 40–60% of the gait cycle and underestimated stifle flexion during 80–90% of the gait cycle.

Conclusions

Considerable magnitudes and effects of STAs on the marker-based 3D gait analysis of dogs were demonstrated. The results indicate that the development of error-compensation techniques based on knowledge regarding STAs is warranted for more accurate gait analysis.

Effects of soft tissue artefacts on computed segmental and stifle kinematics in canine motion analysis

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.

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.

Soft tissue artifact causes significant errors in the calculation of joint angles and range of motion at the hip

Soft tissue movement between reflective skin markers and underlying bone induces errors in gait analysis. These errors are known as soft tissue artifact (STA). Prior studies have not examined how STA affects hip joint angles and range of motion (ROM) during dynamic activities. Herein, we: 1) measured STA of skin markers on the pelvis and thigh during walking, hip abduction and hip rotation, 2) quantified errors in tracking the thigh, pelvis and hip joint angles/ROM, and 3) determined whether model constraints on hip joint degrees of freedom mitigated errors. Eleven asymptomatic young adults were imaged simultaneously with retroreflective skin markers (SM) and dual fluoroscopy (DF), an X-ray technique with sub-millimeter and sub-degree accuracy. STA, defined as the range of SM positions in the DF-measured bone anatomical frame, varied based on marker location, activity and subject. Considering all skin markers and activities, mean STA ranged from 0.3cm to 5.4cm. STA caused the hip joint angle tracked with SM to be 1.9° more extended, 0.6° more adducted, and 5.8° more internally rotated than the hip tracked with DF. ROM was reduced for SM measurements relative to DF, with the largest difference of 21.8° about the internal-external axis during hip rotation. Constraining the model did not consistently reduce angle errors. Our results indicate STA causes substantial errors, particularly for markers tracking the femur and during hip internal-external rotation. This study establishes the need for future research to develop methods minimizing STA of markers on the thigh and pelvis.

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.

In-vivo quantification of dynamic hip joint center errors and soft tissue artifact

Hip joint center (HJC) measurement error can adversely affect predictions from biomechanical models. Soft tissue artifact (STA) may exacerbate HJC errors during dynamic motions. We quantified HJC error and the effect of STA in 11 young, asymptomatic adults during six activities. Subjects were imaged simultaneously with reflective skin markers (SM) and dual fluoroscopy (DF), an x-ray based technique with submillimeter accuracy that does not suffer from STA. Five HJCs were defined from locations of SM using three predictive (i.e., based on regression) and two functional methods; these calculations were repeated using the DF solutions. Hip joint center motion was analyzed during six degrees-of-freedom (default) and three degrees-of-freedom hip joint kinematics. The position of the DF-measured femoral head center (FHC), served as the reference to calculate HJC error. The effect of STA was quantified with mean absolute deviation. HJC errors were (mean±SD) 16.6±8.4mm and 11.7±11.0mm using SM and DF solutions, respectively. HJC errors from SM measurements were all significantly different from the FHC in at least one anatomical direction during multiple activities. The mean absolute deviation of SM-based HJCs was 2.8±0.7mm, which was greater than that for the FHC (0.6±0.1mm), suggesting that STA caused approximately 2.2mm of spurious HJC motion. Constraining the hip joint to three degrees-of-freedom led to approximately 3.1mm of spurious HJC motion. Our results indicate that STA-induced motion of the HJC contributes to the overall error, but inaccuracies inherent with predictive and functional methods appear to be a larger source of error.

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.

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.

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.