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

Marker location and knee joint constraint affect the reporting of overhead squat kinematics in elite youth football players

Motion capture systems are used in the analysis and interpretation of athlete movement patterns for a variety of reasons, but data integrity remains critical regardless. The extent to which marker location or constraining degrees of freedom (DOF) in the biomechanical model impacts on this integrity lacks consensus. Ten elite academy footballers performed bilateral overhead squats using a marker-based motion capture system. Kinematic data were calculated using four different marker sets with 3DOF and 6DOF configurations for the three joint rotations of the right knee. Root mean squared error differences between marker sets ranged in the sagittal plane between 1.02 and 4.19 degrees to larger values in the frontal (1.30-6.39 degrees) and transverse planes (1.33 and 7.97 degrees). The cross-correlation function of the knee kinematic time series for all eight marker-sets ranged from excellent for sagittal plane motion (>0.99) but reduced for both coronal and transverse planes (<0.9). Two-way ANOVA repeated measures calculated at peak knee flexion revealed significant differences between marker sets for frontal and transverse planes (p < 0.05). Pairwise comparisons showed significant differences between some marker sets. Marker location and constraining DOF while measuring relatively large ranges of motion in this population are important considerations for data integrity.

Does IMU redundancy improve multi-body optimization results to obtain lower-body kinematics? A preliminary study says no

Inertial Measurement Units (IMUs) have been proposed as an ecological alternative to optoelectronic systems for obtaining human body joint kinematics. Tremendous work has been done to reduce differences between kinematics obtained with IMUs and optoelectronic systems, by improving sensor-to-segment calibration, fusion algorithms, and by using Multibody Kinematics Optimization (MKO). However, these improvements seem to reach a barrier, particularly on transverse and frontal planes. Inspired by marker-based MKO approach performed via OpenSim, this study proposes to test whether IMU redundancy with MKO could improve lower-limb kinematics obtained from IMUs. For this study, five subjects were equipped with 11 IMUs and 30 reflective markers tracked by 18 optoelectronic cameras. They then performed gait, cycling, and running actions. Four different lower-limb kinematics were computed: one kinematics based on markers after MKO, one kinematics based on IMUs without MKO, and two based on IMUs after MKO performed with OpenSense (one with, and one without, sensor redundancy). Kinematics were compared via Root Mean Square Difference and correlation coefficients to kinematics based on markers after MKO. Results showed that redundancy does not reduce differences with the kinematics based on markers after MKO on frontal and transverse planes comparatively to classic IMU MKO. Sensor redundancy does not seem to impact lower-limb kinematics on frontal and transverse planes, due to the likelihood of the "rigid component" of soft-tissue artefact impacting all sensors located on one segment.

Sensor-to-Bone Calibration with the Fusion of IMU and Bi-Plane X-rays

Inertial measurement units (IMUs) need sensor-to-segment calibration to measure human kinematics. Multiple methods exist, but, when assessing populations with locomotor function pathologies, multiple limitations arise, including holding postures (limited by joint pain and stiffness), performing specific tasks (limited by lack of selectivity) or hypothesis on limb alignment (limited by bone deformity and joint stiffness). We propose a sensor-to-bone calibration based on bi-plane X-rays and a specifically designed fusion box to measure IMU orientation with respect to underlying bones. Eight patients undergoing total hip arthroplasty with bi-plane X-rays in their clinical pathway participated in the study. Patients underwent bi-plane X-rays with fusion box and skin markers followed by a gait analysis with IMUs and a marker-based method. The validity of the pelvis, thigh and hip kinematics measured with a conventional sensor-to-segment calibration and with the sensor-to-bone calibration were compared. Results showed (1) the feasibility of the fusion of bi-plane X-rays and IMUs in measuring the orientation of anatomical axes, and (2) higher validity of the sensor-to-bone calibration for the pelvic tilt and similar validity for other degrees of freedom. The main strength of this novel calibration is to remove conventional hypotheses on joint and segment orientations that are frequently violated in pathological populations.

On the accuracy of the Conventional gait Model: Distinction between marker misplacement and soft tissue artefact errors

There is a lack of knowledge about the accuracy of the Conventional Gait Model (CGM), compared to the true bone motion. Accuracy is hindered by both marker misplacement and soft-tissue artefact (STA). The effect of the lateral knee marker (KNE) misplacement and STA was determined from a secondary analysis of 13 subjects equipped with a total knee prothesis for which simultaneous dual-plane fluoroscopy and marker-based motion capture was available. In average, STA alone led to 3.3°, 2.9° and 6.7° errors for knee flexion, knee abduction, and the absolute hip rotation respectively. In comparison, marker misplacement led to 0.9°, 4.0° and 12.3° errors for the same kinematics. We showed that STA alone may lead to knee flexion-adduction cross-talk. This finding has clinical repercussions for the use of knee cross talk as a qualitative indicator of knee axis alignment. Our study showed that cumulative effects of marker misplacement and STA affect the transverse plane angles, making challenging to track internal/external rotation with less than 5° of errors.

Validation of 3D Knee Kinematics during Gait on Treadmill with an Instrumented Knee Brace

To test a novel instrumented knee brace intended for use as a rehabilitation system, based on inertial measurement units (IMU) to monitor home-based exercises, the device was compared to the gold standard of motion analysis. The purpose was to validate a new calibration method through functional tasks and assessed the value of adding magnetometers for motion analysis. Thirteen healthy young adults performed a 60-second gait test at a comfortable walking speed on a treadmill. Knee kinematics were captured simultaneously, using the instrumented knee brace and an optoelectronic camera system (OCS). The intraclass correlation coefficient (ICC) showed excellent reliability for the three axes of rotation with and without magnetometers, with values ranging between 0.900 and 0.972. Pearson's r coefficient showed good to excellent correlation for the three axes, with the root mean square error (RMSE) under 3° with the IMUs and slightly higher with the magnetometers. The instrumented knee brace obtained certain clinical parameters, as did the OCS. The instrumented knee brace seems to be a valid tool to assess ambulatory knee kinematics, with an RMSE of <3°, which is sufficient for clinical interpretations. Indeed, this portable system can obtain certain clinical parameters just as well as the gold standard of motion analysis. However, the addition of magnetometers showed no significant advantage in terms of enhancing accuracy.

AGREEMENT OF HIP KINEMATICS BETWEEN TWO TRACKING MARKER CONFIGURATIONS USED WITH THE CODA PELVIS DURING ERGONOMIC ROOFING TASKS

The anterior and posterior iliac spine markers frequently used to define the pelvis, are commonly occluded during three-dimensional (3D) motion capture. The occlusion of these markers leads to the use of various tracking marker configurations on the pelvis, which affect kinematic results. The purpose of this investigation was to examine the agreement of CODA pelvis kinematic results when two different tracking marker configurations were used during roofing tasks. 3D motion data were collected on seven male subjects while mimicking two roofing tasks. Hip joint angles (HJAs) were computed using the CODA pelvis with two different tracking marker configurations, the trochanter tracking method (TTM), and virtual pelvis tracking method (VPTM). Agreement between tracking marker configurations was assessed using cross-correlations, bivariate correlations, mean absolute differences (MADs), and Bland-Altman (BA) plots. The correlations displayed no time lag and strong agreement (all r > 0.83) between the HJA from the VPTM and TTM, suggesting the timing occurrence of variables are comparable between the two tracking marker configurations. The MAD between the VPTM and TTM displayed magnitude differences, but most of the differences were within a clinically acceptable range. Caution should still be used when comparing kinematic results between various tracking marker configurations, as differences exist.

A novel method for in vivo measurement of dynamic ischiofemoral space based on MRI and motion capture

Purpose: To use a novel in vivo method to simulate a moving hip model. Then, measure the dynamic bone-to-bone distance, and analyze the ischiofemoral space (IFS) of patients diagnosed with ischiofemoral impingement syndrome (IFI) during dynamic activities. Methods: Nine healthy subjects and 9 patients with IFI were recruited to collect MRI images and motion capture data. The motion trail of the hip during motion capture was matched to a personalized 3D hip model reconstructed from MRI images to get a dynamic bone model. This personalized dynamic in vivo method was then used to simulate the bone motion in dynamic activities. Validation was conducted on a 3D-printed sphere by comparing the calculated data using this novel method with the actual measured moving data using motion capture. Moreover, the novel method was used to analyze the in vivo dynamic IFS between healthy subjects and IFI patients during normal and long stride walking. Results: The validation results show that the root mean square error (RMSE) of slide and rotation was 1.42 mm/1.84° and 1.58 mm/2.19°, respectively. During normal walking, the in vivo dynamic IFS was significantly larger in healthy hips (ranged between 15.09 and 50.24 mm) compared with affected hips (between 10.16 and 39.74 mm) in 40.27%-83.81% of the gait cycle (p = 0.027). During long stride walking, the in vivo dynamic IFS was also significantly larger in healthy hips (ranged between 13.02 and 51.99 mm) than affected hips (between 9.63 and 44.22 mm) in 0%-5.85% of the gait cycle (p = 0.049). Additionally, the IFS of normal walking was significantly smaller than long stride walking during 0%-14.05% and 85.07%-100% of the gait cycle (p = 0.033, 0.033) in healthy hips. However, there was no difference between the two methods of walking among the patients. Conclusions: This study established a novel in vivo method to measure the dynamic bone-to-bone distance and was well validated. This method was used to measure the IFS of patients diagnosed with IFI, and the results showed that the IFS of patients is smaller compared with healthy subjects, whether in normal or long stride walking. Meanwhile, IFI eliminated the difference between normal and long stride walking.

A new marker cluster anchored to the iliotibial band improves tracking of hip and thigh axial rotations

Tracking hip and thigh axial rotation has limited accuracy due to the large soft tissue artifact. We proposed a tracking-markers cluster anchored to the prominent distal part of the iliotibial band (ITB) to improve thigh tracking. We investigated if the ITB cluster improves accuracy compared with a traditionally used thigh cluster. We also compared the hip kinematics obtained with these clusters during walking and step-down. Hip and thigh kinematics were assessed during a task of active internal-external rotation with the knee extended, in which the shank rotation is a reference due to smaller soft-tissue artifact. Errors of the hip and thigh axial rotations obtained with the thigh clusters compared to the shank cluster were computed as root-mean-square errors, which were compared by paired t-tests. The angular waveforms of this task were compared using the statistical parametric mapping (SPM). Additionally, the hip waveforms in all planes obtained with the thigh clusters were compared during walking and step-down, using Coefficients of Multiple Correlation (CMC) and SPM (α = 0.05 for all analyses). The ITB cluster errors were approximately 25 % smaller than the traditional cluster error (p < 0.001). ITB cluster errors were smaller at external rotation angles while the traditional cluster error was smaller at internal rotation angles (p < 0.001), although the clusters' waveforms were not significantly different (p ≥ 0.005). During walking and step-down, both clusters provided similar hip kinematics (CMC ≥ 0.75), but differences were observed in parts of the cycles (p ≤ 0.04). The findings suggest that the ITB cluster may be used in studies focused on hip axial rotation.

The Conventional Gait Model’s sensitivity to lower-limb marker placement

Clinical gait analysis supports treatment decisions for patients with motor disorders. Measurement reproducibility is affected by extrinsic errors such as marker misplacement—considered the main factor in gait analysis variability. However, how marker placement affects output kinematics is not completely understood. The present study aimed to evaluate the Conventional Gait Model’s sensitivity to marker placement. Using a dataset of kinematics for 20 children, eight lower-limb markers were virtually displaced by 10 mm in all four planes, and all the displacement combinations were recalculated. Root-mean-square deviation angles were calculated for each simulation with respect to the original kinematics. The marker movements with the greatest impact were for the femoral and tibial wands together with the lateral femoral epicondyle marker when displaced in the anterior–posterior axis. When displaced alone, the femoral wand was responsible for a deviation of 7.3° (± 1.8°) in hip rotation. Transversal plane measurements were affected most, with around 40% of simulations resulting in an effect greater than the acceptable limit of 5°. This study also provided insight into which markers need to be placed very carefully to obtain more reliable gait data.

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.

Smart sensor tights: Movement tracking of the lower limbs in football

This article presents a novel smart sensor garment with integrated miniaturized inertial measurements units (IMUs) that can be used to monitor lower body kinematics during daily training activities, without the need of extensive technical assistance throughout the measurements. The smart sensor tights enclose five ultra-light sensor modules that measure linear accelerations, angular velocities, and the earth magnetic field in three directions. The modules are located at the pelvis, thighs, and shanks. The garment enables continuous measurement in the field at high sample rates (250 Hz) and the sensors have a large measurement range (32 g, 4,000°/s). They are read out by a central processing unit through an SPI bus, and connected to a centralized battery in the waistband. A fully functioning prototype was built to perform validation studies in a lab setting and in a field setting. In the lab validation study, the IMU data (converted to limb orientation data) were compared with the kinematic data of an optoelectronic measurement system and good validity (CMCs >0.8) was shown. In the field tests, participants experienced the tights as comfortable to wear and they did not feel restricted in their movements. These results show the potential of using the smart sensor tights on a regular base to derive lower limb kinematics in the field.

Estimating soft tissue artifact of the thigh in high knee flexion tasks using optical motion Capture: Implications for marker cluster placement

Soft tissue artifact in motion capture is widely accepted as a significant source of error in kinematic and kinetic measurements. Non-invasive methods of estimating soft tissue artifact, those requiring only motion capture, provide a feasible method to evaluate marker placement on a segment and enable recommendations for marker configurations which can minimize soft tissue artifact. The purpose of this study was to investigate the effect of thigh marker cluster location on soft tissue artifact during high knee flexion (>120 deg) as unique deformation of soft tissue occurs in this range (e.g. thigh-calf contact). Motion of the pelvis and lower limbs were recorded during squatting and kneeling in fifty participants. Six rigid marker clusters were affixed to the skin on the anterior, lateral, and anterolateral aspect, at the distal and middle third of the thighs. To estimate soft tissue artifact, the functional hip joint center was reconstructed relative to the pelvis cluster and each of the six thigh clusters throughout motion. The difference in the position of these two points was input into Bland-Altman analyses and compared between the thigh clusters. Across the tasks, the total mean difference ranged from 2.81 to 8.95 cm while the lower and upper limits of agreement ranged from -0.79 to 2.54 cm and 5.04 to 17.65 cm, respectively. Using this non-invasive method, the mid-anterolateral cluster was least susceptible to soft tissue artifact and thus would be recommended, while the lateral clusters were most susceptible and should avoided in high knee flexion and similar tasks.

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.

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.

Evaluation of a multibody kinematics optimization method for three-dimensional canine pelvic limb gait analysis

BACKGROUND

Skin marker-based three-dimensional kinematic gait analysis were commonly used to assess the functional performance and movement biomechanics of the pelvic limb in dogs. Unfortunately, soft tissue artefact would compromise the accuracy of the reproduced pelvic limb kinematics. Multibody kinematics optimization framework was often employed to compensate the soft tissue artefact for a more accurate description of human joint kinematics, but its performance on the determination of canine pelvic limb skeletal kinematics has never been evaluated. This study aimed to evaluate a multibody kinematics optimization framework used for the determination of canine pelvic limb kinematics during gait by comparing its results to those obtained using computed tomography model-based fluoroscopy analysis.

RESULTS

Eight clinically normal dogs were enrolled in the study. Fluoroscopy videos of the stifle joint and skin marker trajectories were acquired when the dogs walked on a treadmill. The pelvic limb kinematics were reconstructed through marker-based multibody kinematics optimization and single-body optimization. The reference kinematics data were derived via a model-based fluoroscopy analysis. The use of multibody kinematics optimization yielded a significantly more accurate estimation of flexion/extension of the hip and stifle joints than the use of single-body optimization. The accuracy of the joint model parameters and the weightings to individual markers both influenced the soft tissue artefact compensation capability.

CONCLUSIONS

Multibody kinematics optimization designated for soft tissue artefact compensation was established and evaluated for its performance on canine gait analysis, which provided a further step in more accurately describing sagittal plane kinematics of the hip and stifle joints.

Knee kinematics after cruciate retaining highly congruent mobile bearing total knee arthroplasty: An in vivo dynamic RSA study

PURPOSE

This work presents a kinematic evaluation of a cruciate retaining highly congruent mobile bearing total knee arthroplasty design using dynamic Roentgen sterephotogrammetric analysis. The aim was to understand the effect of this implant design on the kinematics of prosthetic knees during dynamic activities.

METHODS

A cohort of 15 patients was evaluated at nine month follow-up after surgery. The mean age was 74.8 (range 66-85) years. The kinematics was evaluated using the Grood and Suntay decomposition and the Low-Point (LP) methods.

RESULTS

?tlsb=-0.15pt?>From sitting to standing up position, the femoral component internally rotated (from -11.3 ± 0.2° to -7.0 ± 0.2°). Varus-valgus rotations were very close to 0° during the whole motor task. LP of medial condyle moved from an anterior position of 12.0 ± 0.2 mm to a posterior position of -12.4 ± 0.2 mm; LP of the lateral condyle moved from an anterior position of 8.1 ± 0.2 mm to a posterior position of -12.4 ± 0.2 mm, showing a bi-condylar rollback where both condyles moved parallel backward. Moreover, the femoral component showed anterior translation with respect to the tibia from 80° to 20° (from -4.9 ± 0.2 mm to 3.3 ± 0.2 mm), then a posterior translation from 20° to full extension was identified (from 3.3 ± 0.2 mm to 0.5 ± 0.2 mm).

CONCLUSIONS

Paradoxical anterior femoral translation and absence of medial-pivoting motion were recorded, highlighting the role of the symmetric deep dishes insert as main driver of the kinematic of this TKA design.

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.

Validity of inertial sensor based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements

3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°– 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°– 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings.

Quantification of three-dimensional soft tissue artifacts in the canine hindlimb during passive stifle motion

Background

Three-dimensional joint kinematics during canine locomotion are commonly measured using skin marker-based stereophotogrammetry technologies. However, marker-related errors caused by the displacement of the skin surface relative to the underlying bones (i.e., soft tissue artifacts, STA) may affect the accuracy of the measurements and obscure clinically relevant information. Few studies have assessed STA in canine limbs during kinematic analysis. The magnitudes and patterns of the STA and their influence on kinematic analysis remain unclear. Therefore, the current study aims to quantify the in vivo STA of skin markers on the canine thigh and crus during passive joint motion. The stifle joints of ten dogs were passively extended while the skin markers were measured using a motion capture system, and skeletal kinematics were determined using a CT-to-fluoroscopic image registration method.

Results

The skin markers exhibited considerable STA relative to the underlying bones, with a peak amplitude of 27.4 mm for thigh markers and 28.7 mm for crus markers; however, the amplitudes and displacement directions at different attachment sites were inconsistent. The markers on the cranial thigh and lateral crus closer to the stifle joint had greater STA amplitudes in comparison to those of other markers. Most markers had STA with linear and quadratic patterns against the stifle flexion angles. These STA resulted in underestimated flexion angles but overestimated adduction and internal rotation when the stifle was flexed to greater than 90°.

Conclusions

Marker displacements relative to the underlying bones were prominent in the cranial aspect of the thigh and the proximal-lateral aspect of the crus. The calculated stifle kinematic variables were also affected by the STA. These findings can provide a reference for marker selection in canine motion analysis for similar motion tasks and clarify the relationship between STA patterns and stifle kinematics; the results may therefore contribute to the development of STA models and compensation techniques for canine motion analysis.

Electronic supplementary material

The online version of this article (10.1186/s12917-018-1714-7) contains supplementary material, which is available to authorized users.

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.

Joint Center Estimation Using Single-Frame Optimization: Part 1: Numerical Simulation

The biomechanical models used to refine and stabilize motion capture processes are almost invariably driven by joint center estimates, and any errors in joint center calculation carry over and can be compounded when calculating joint kinematics. Unfortunately, accurate determination of joint centers is a complex task, primarily due to measurements being contaminated by soft-tissue artifact (STA). This paper proposes a novel approach to joint center estimation implemented via sequential application of single-frame optimization (SFO). First, the method minimizes the variance of individual time frames’ joint center estimations via the developed variance minimization method to obtain accurate overall initial conditions. These initial conditions are used to stabilize an optimization-based linearization of human motion that determines a time-varying joint center estimation. In this manner, the complex and nonlinear behavior of human motion contaminated by STA can be captured as a continuous series of unique rigid-body realizations without requiring a complex analytical model to describe the behavior of STA. This article intends to offer proof of concept, and the presented method must be further developed before it can be reasonably applied to human motion. Numerical simulations were introduced to verify and substantiate the efficacy of the proposed methodology. When directly compared with a state-of-the-art inertial method, SFO reduced the error due to soft-tissue artifact in all cases by more than 45%. Instead of producing a single vector value to describe the joint center location during a motion capture trial as existing methods often do, the proposed method produced time-varying solutions that were highly correlated (r > 0.82) with the true, time-varying joint center solution.

Relationship between Kellgren-Lawrence score and 3D kinematic gait analysis of patients with medial knee osteoarthritis using a new gait system

Knee osteoarthritis (KOA) is reported to have characteristic kinematics during walking. However, the relationship between Kellgren-Lawrence (K/L) score and the 3D kinematic gait of patients with medial KOA remains unclear. Here, ninety-seven patients with medial KOA and thirty-eight asymptomatic participants were involved. Patients with medial KOA were divided into early, moderate, and severe KOA based on the K/L score. Through kinematic gait analysis, we found a relationship between K/L score and 3D kinematic gait for patients. All KOA knees had a significantly reduced range of motion. As the K/L score was increasing, the knee flexion at the heel strike and 50% of the stance phase increased while the peak knee flexion in the swing phase decreased. In addition, the adduction and femoral rotation increased internally at the heel strike, 50% of the stance phase, and maximum angle of the swing phase. Femoral translation increased anteriorly and distally at the heel strike and 50% of the stance phase. The severe group had more medial translation than the asymptomatic groups. Significant alterations of three-dimensional joint kinematics were identified in subjects suffering various severities in Chinese patients. This study provides an important reference for the treatment options, therapy assessment, and rehabilitation of KOA.

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.

Soft tissue artefacts of skin markers on the lower limb during cycling: Effects of joint angles and pedal resistance

Soft tissue artefacts (STA) are a major error source in skin marker-based measurement of human movement, and are difficult to eliminate non-invasively. The current study quantified in vivo the STA of skin markers on the thigh and shank during cycling, and studied the effects of knee angles and pedal resistance by using integrated 3D fluoroscopy and stereophotogrammetry. Fifteen young healthy adults performed stationary cycling with and without pedal resistance, while the marker data were measured using a motion capture system, and the motions of the femur and tibia/fibula were recorded using a bi-plane fluoroscopy-to-CT registration method. The STAs with respect to crank and knee angles over the pedaling cycle, as well as the within-cycle variations, were obtained and compared between resistance conditions. The thigh markers showed greater STA than the shank ones, the latter varying linearly with adjacent joint angles, the former non-linearly with greater within-cycle variability. Both STA magnitudes and within-cycle variability were significantly affected by pedal resistance (p<0.05). The STAs appeared to be composed of one component providing the stable and consistent STA patterns and another causing their variations. Mid-segment markers experienced smaller STA ranges than those closer to a joint, but tended to have greater variations primarily associated with pedal resistance and muscle contractions. The current data will be helpful for a better choice of marker positions for data collection, and for developing methods to compensate for both stable and variation components of the STA.

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.

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.