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Copyright of Izmir Katip Celebi University Faculty of Health Sciences Journal / İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi is the property of Izmir Katip Celebi University, Faculty of Health Sciene Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Bone shapes strongly influence force and moment predictions of kinematic and musculoskeletal models used in motion analysis. The precise determination of joint reference frames is essential for accurate predictions. Since clinical motion analysis typically does not include medical imaging, from which bone shapes may be obtained, scaling methods using reference subjects to create subject-specific bone geometries are widely used.Research Question: This study investigated if lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, can be used to improve conventional linear scaling methods of bone geometries.Methods: SSMs created from 35 healthy adult femurs and tibiae/fibulae were used to reconstruct bone shapes by minimising the distance between anatomical landmarks on the models and those digitised in the motion laboratory or on medical images. Soft tissue artefacts were quantified from magnetic resonance images and then used to predict distances between landmarks digitised on the skin surface and bone. Reconstruction results were compared to linearly scaled models by measuring root mean squared distances to segmented surfaces, calculating differences of commonly used anatomical measures and the errors in the prediction of the hip joint centre.Results: SSM reconstructed surface predictions from varying landmark sets from skin and bone landmarks were more accurate compared to linear scaling methods (2.60-2.95 mm vs. 3.66-3.87 mm median error; p < 0.05). No significant differences were found between SSM reconstructions from bony landmarks and SSM reconstructions from digitised landmarks obtained in the motion lab and therefore reconstructions using skin landmarks are as accurate as reconstructions from landmarks obtained from medical images.Significance: These results indicate that SSM reconstructions can be used to increase the accuracy in obtaining bone shapes from surface digitised experimental data acquired in motion lab environments. [ABSTRACT FROM AUTHOR]

Abstract The Conventional Gait Model (CGM) needs to benefit from large investigations on localization of the hip joint centre (HJC). Incorrect positions from the native equations were demonstrated (Sangeux et al., 2014; Harrington et al., 2007). More accurate equations were proposed but their impact on kinematics and kinetic CGM outputs was never evaluated. This short communication aims at examining if adoption of new HJC equations would alter standard CGM outputs. Sixteen able bodied participants underwent a full 3-D optoelectronic gait analysis followed by a 3-D ultrasound localization of their hips. Data were processed through the open source python package pyCGM2 replicating kinematic and kinetic processing of the native CGM. Compared with 3D ultrasound location, Hara equations improved the accuracy of sagittal plane kinematics (0.6°) and kinetics (0.02 N m kg −1) for the hip. The worst case participant exhibited Harrington's equations reached a deviation of 3° for the sagittal kinematics. In the coronal plane, Hara and Harrington equations presented similar differences (1°) for the hip whilst Davis equations had the largest deviation for hip abduction (2.7°) and hip abductor moment (0.10 N m kg −1). Both Harrington and Hara equations improved the CGM location of the HJC. Hara equations improved results in the sagittal plane, plus utilise a single anthropometrics measurement, leg length, that may be more robust. However, neither set of equations had significant effect on kinematics. We reported some effects on kinetics, particularly in the coronal plane, which warrant caution in interpreting outputs using different sets of equations. [ABSTRACT FROM AUTHOR]

Abstract Marker-based dynamic functional or regression methods are used to compute joint centre locations that can be used to improve linear scaling of the pelvis in musculoskeletal models, although large errors have been reported using these methods. This study aimed to investigate if statistical shape models could improve prediction of the hip joint centre (HJC) location. The inclusion of complete pelvis imaging data from computed tomography (CT) was also explored to determine if free-form deformation techniques could further improve HJC estimates. Mean Euclidean distance errors were calculated between HJC from CT and estimates from shape modelling methods, and functional- and regression-based linear scaling approaches. The HJC of a generic musculoskeletal model was also perturbed to compute the root-mean squared error (RMSE) of the hip muscle moment arms between the reference HJC obtained from CT and the different scaling methods. Shape modelling without medical imaging data significantly reduced HJC location error estimates (11.4 ± 3.3 mm) compared to functional (36.9 ± 17.5 mm, p = <0.001) and regression (31.2 ± 15 mm, p = <0.001) methods. The addition of complete pelvis imaging data to the shape modelling workflow further reduced HJC error estimates compared to no imaging (6.6 ± 3.1 mm, p = 0.002). Average RMSE were greatest for the hip flexor and extensor muscle groups using the functional (16.71 mm and 8.87 mm respectively) and regression methods (16.15 mm and 9.97 mm respectively). The effects on moment-arms were less substantial for the shape modelling methods, ranging from 0.05 to 3.2 mm. Shape modelling methods improved HJC location and muscle moment-arm estimates compared to linear scaling of musculoskeletal models in patients with hip osteoarthritis. [ABSTRACT FROM AUTHOR]

Joint kinematics can be calculated by Direct Kinematics (DK), which is used in most clinical gait laboratories, or Inverse Kinematics (IK), which is mainly used for musculoskeletal research. In both approaches, joint centre locations are required to compute joint angles. The hip joint centre (HJC) in DK models can be estimated using predictive or functional methods, while in IK models can be obtained by scaling generic models. The aim of the current study was to systematically investigate the impact of HJC location errors on lower limb joint kinematics of a clinical population using DK and IK approaches. Subject-specific kinematic models of eight children with cerebral palsy were built from magnetic resonance images and used as reference models. HJC was then perturbed in 6mm steps within a 60mm cubic grid, and kinematic waveforms were calculated for the reference and perturbed models. HJC perturbations affected only hip and knee joint kinematics in a DK framework, but all joint angles were affected when using IK. In the DK model, joint constraints increased the sensitivity of joint range-of-motion to HJC location errors. Mean joint angle offsets larger than 5° were observed for both approaches (DK and IK), which were larger than previously reported for healthy adults. In the absence of medical images to identify the HJC, predictive or functional methods with small errors in anterior-posterior and medial-lateral directions and scaling procedures minimizing HJC location errors in the anterior-posterior direction should be chosen to minimize the impact on joint kinematics. [ABSTRACT FROM AUTHOR]

In human motion analysis predictive or functional methods are used to estimate the location of the hip joint centre (HJC). It has been shown that the Harrington regression equations (HRE) and geometric sphere fit (GSF) method are the most accurate predictive and functional methods, respectively. To date, the comparative reliability of both approaches has not been assessed. The aims of this study were to (1) compare the reliability of the HRE and the GSF methods, (2) analyse the impact of the number of thigh markers used in the GSF method on the reliability, (3) evaluate how alterations to the movements that comprise the functional trials impact HJC estimations using the GSF method, and (4) assess the influence of the initial guess in the GSF method on the HJC estimation. Fourteen healthy adults were tested on two occasions using a three-dimensional motion capturing system. Skin surface marker positions were acquired while participants performed quite stance, perturbed and non-perturbed functional trials, and walking trials. Results showed that the HRE were more reliable in locating the HJC than the GSF method. However, comparison of inter-session hip kinematics during gait did not show any significant difference between the approaches. Different initial guesses in the GSF method did not result in significant differences in the final HJC location. The GSF method was sensitive to the functional trial performance and therefore it is important to standardize the functional trial performance to ensure a repeatable estimate of the HJC when using the GSF method. [ABSTRACT FROM AUTHOR]

Accurate estimation of the hip joint centre (HJC) location is critical for modelling the kinematics and kinetics of the lower limb. Regression equations are commonly used to predict the HJC from anatomical landmarks on the pelvis, such as those published by Tylkowski et al., Andriacchi et al., Bell et al., and Seidel et al. Using a population of 159 CT-segmented pelvises, we assessed the accuracy of these methods as originally reported, and refined their parameters based on our larger cohort. We found the Tylkowski, Bell, and Seidel methods had mean Euclidean errors of 22.5, 26.4, and 17.9 mm, respectively. With new parameters for each method 'back-calculated' from our pelvic population, each method's error was reduced by an average of 69 %, with mean absolute errors of 7.9, 6.6, and 5.9 mm, respectively. For all methods, error has been reduced to below 1 cm, well below published levels for pelvic landmark estimation methods. These results highlight the need to validate and re-calibrate joint centre prediction methods on large, representative datasets to account for natural morphological variations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hip joint centre (HJC) regression equation error during paediatric gait has recently been shown to have clinical significance. In relation to adult gait, it has been inferred that comparable errors with children in absolute HJC position may in fact result in less significant kinematic and kinetic error. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak) for adult subjects against the equations of Harrington et al. The relationship between HJC position error and subject size was also investigated for the Davis et al. set. Full 3-dimensional gait analysis was performed on 12 healthy adult subjects with data for each set compared to Harrington et al. The Gait Profile Score, Gait Variable Score and GDI-kinetic were used to assess clinical significance while differences in HJC position between the Davis and Harrington sets were compared to leg length and subject height using regression analysis. A number of statistically significant differences were present in absolute HJC position. However, all sets fell below the clinically significant thresholds (GPS <1.6°, GDI-Kinetic <3.6 points). Linear regression revealed a statistically significant relationship for both increasing leg length and increasing subject height with decreasing error in anterior/posterior and superior/inferior directions. Results confirm a negligible clinical error for adult subjects suggesting that any of the examined sets could be used interchangeably. Decreasing error with both increasing leg length and increasing subject height suggests that the Davis set should be used cautiously on smaller subjects. [ABSTRACT FROM AUTHOR]

Determination of the hip joint centre (HJC) is important to accurately estimate hip joint motion, moments and muscle forces. The most accurate method for HJC estimation without medical imaging is an area of interest in the biomechanics community, especially in a paediatric population, which has not been widely evaluated. HJC locations were calculated by sphere-fitting to the acetabulum of three-dimensional pelvises segmented from 333 CT scans of children aged 4 to 18 years old. Three methods for determining the HJC were compared: regression equations, linear scaling, and shape model prediction. The new regression equations developed in this study produced Euclidean distance errors of 6.23 mm ± 2.90 mm. Linear scaling of paediatric bone produced errors of 3.90 mm ± 2.52 mm and adult bone scaling of 5.45 mm ± 3.26 mm. Prediction of the HJC using a paediatric statistical shape model produced mean Euclidian distance errors of 2.95 mm ± 1.65 mm. Overall, shape model prediction of the HJC produced the lowest errors, with linear scaling of a mean paediatric pelvis providing better estimates than regression equations. [ABSTRACT FROM AUTHOR]

The purpose of this short communication is to discuss the relative benefits of various anthropometric parameters to drive predictive equations to locate the hip joint centres. The effect of soft tissue thickness over the anterior and posterior superior iliac spines on pelvic depth, pelvic width and leg length and position of the hip joint centres was discussed theoretically and experimentally, from a secondary analysis of previously published data. Results highlighted that anthropometric measurements of pelvic width and leg length were similar when obtained from MRI images or during gait analysis whereas pelvic depth was different. The secondary analysis showed that Harrington et al. [5] equations using either only pelvic width or only leg length would lead to 3mm improvement, in average over 164 limbs, over the equations using the best anthropometric predictors from MRI data. [ABSTRACT FROM AUTHOR]

Background Inaccuracies in locating the three-dimensional position of the hip joint centre affect the calculated hip and knee kinematics, force- and moment-generating capacity of muscles and hip joint mechanics, which can lead to incorrect interpretations and recommendations in gait analysis. Several functional and predictive methods have been developed to estimate the hip joint centre location, and the International Society of Biomechanics recommends a functional approach for use with participants that have adequate range of motion at the hip, and predictive methods in those with insufficient range of motion. The purpose of the current systematic review was to substantiate the International Society of Biomechanics recommendations. This included identifying the most accurate functional and predictive methods, and defining ‘adequate’ range of motion. Methods A systematic search with broad search terms was performed including five databases. Findings The systematic search yielded to 801 articles, of which 34 papers were included. Eleven different predictive and 13 different functional methods were identified. The results showed that the geometric sphere fit method and Harrington equations are the most accurate functional and predictive approaches respectively that have been evaluated in vivo. Interpretation In regard to the International Society of Biomechanics recommendations, the geometric sphere fit method should be used in people with sufficient active hip range of motion and the Harrington equations should be used in patients without sufficient hip range of motion. Multi-plane movement trials with at least 60° of flexion–extension and 30° of ab-adduction range of motion are suggested when using functional methods. [ABSTRACT FROM AUTHOR]

Regression equations based on pelvic anatomy are routinely used to estimate the hip joint centre during gait analysis. While the associated errors have been well documented, the clinical significance of these errors has not been reported. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak software) against the equations of Harrington et al. Full 3-dimensional gait analysis was performed on 18 healthy paediatric subjects. Kinematic and kinetic data were calculated using each set of regression equations and compared to Harrington et al. In addition, the Gait Profile Score and GDI-Kinetic were used to assess clinical significance. Bell et al. was the best performing set with differences in Gait Profile Score (0.13°) and GDI-Kinetic (0.84 points) falling below the clinical significance threshold. Small deviations were present for the Orthotrak set for hip abduction moment (0.1 Nm/kg), however differences in Gait Profile Score (0.27°) and GDI-Kinetic (2.26 points) remained below the clinical threshold. Davis et al. showed least agreement with a clinically significant difference in GDI-Kinetic score (4.36 points). It is proposed that Harrington et al. or Bell et al. regression equation sets are used during gait analysis especially where inverse dynamic data are calculated. Orthotrak is a clinically acceptable alternative however clinicians must be aware of the effects of error on hip abduction moment. The Davis et al. set should be used with caution for inverse dynamic analysis as error could be considered clinically meaningful. [ABSTRACT FROM AUTHOR]

Identification of the hip joint centre (HJC) is important in the biomechanical examination of human movement. However, there is yet to be any published information regarding the influence of different HJC locations on hip and knee joint kinetics during functional tasks. This study aimed to examine the influence of four different HJC techniques on 3- D hip and knee joint kinetics/kinematics during the squat. Hip and knee joint kinetics/kinematics of the squat were obtained from fifteen male participants using an eight camera motion capture system. The 3-D kinetics/kinematics of the squat were quantified using four hip joint centre estimation techniques. Repeated measures ANOVAs were used to compare the discrete parameters as a function of each HJC location. The results show that significant differences in joint angles and moment parameters were evident at both the hip and knee joint in the coronal and transverse planes. These observations indicate that when calculating non-sagittal joint kinetics/kinematics during the squat, researchers should carefully consider their HJC method as it may significantly affect the interpretation of their data. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Highlights: [•] Accuracy of predictive and functional hip joint centre estimates against EOS. [•] Functional geometrical sphere fitting technique; 1.1cm from EOS. [•] Harrington et al. predictive equations; 1.7cm from EOS. [•] Predictive equations best suited for population with hip/motor control problems. [ABSTRACT FROM AUTHOR]

Copyright of Movement & Sport Sciences / Science & Motricité is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate estimation of the hip joint centre (HJC) in gait analysis and in computer assisted orthopaedic procedures is a basic requirement. Functional methods, based on rigid body localisation, assessing the kinematics of the femur during circumduction movements (pivoting) have been used for estimating the HJC. Localising the femoral segment only, as it is usually done in total knee replacement procedure, can give rise to estimation errors, since the pelvis, during the passive pivoting manoeuvre, might undergo spatial displacements. This paper presents the design and test of an unscented Kalman filter that allows the estimation of the HJC by observing the pose of the femur and the 3D coordinates of a single marker attached to the pelvis. This new approach was validated using a hip joint mechanical simulator, mimicking both hard and soft tissues. The algorithm performances were compared with the literature standards and proved to have better performances in case of pelvis translation greater than 8 mm, thus satisfying the clinical requirements of the application. [ABSTRACT FROM AUTHOR]

Abstract: Functional hip joint center (HJC) calculation involves recording movements of femur relative to acetabulum through markers placed on skin around thigh and pelvis. This non-invasive method of finding hip joint center involves either fitting a geometric sphere onto marker trajectories or coordinate transformation techniques which find the point with least movement in local frame with respect to global frame. A survey study by Ehrig et al has evaluated both categories of formal methods through virtual simulation and also contributed another algorithm known as “SCoRE”(Systematic center of rotation estimation). This algorithm gives an accuracy of 0.5cm with 20 degree range of motion (ROM) and claimed to be most accurate with both segments in motion. This paper reviews the studies using this method to calculate hip joint center. Also a review of studies using Ultrasound as a validation method has been provided. This forms the basis to the possibility of using Ultrasonic sensors to be placed along with markers to measure the relative movement of markers with respect to bone in vivo. This paper provides a survey of studies performed on human subjects either in vivo (live humans) or ex vivo (cadaver) to help an experimenter or researcher pick the best relevant technique matching their experimentation requirement including soft tissue artifact factor. [Copyright &y& Elsevier]

Purpose. The lunge is a fundamental offensive fencing technique, common to all contemporary fencing styles. Therefore, when using 3-D kinematic analysis to quantify lower extremity rotations during the fencing lunge, it is important for researchers to correctly interpret this movement. Locating the centre of the hip is required to accurately quantify hip and knee joint rotations, with three non-invasive techniques using anatomical, functional and projection methods currently available for the estimation of hip joint centre. This study investigated the influence of these three techniques on hip and knee joint kinematics during the fencing lunge. Methods. Three-dimensional kinematics of the lunge were collected from 13 experienced epee fencers using an eight-camera motion capture system. The 3-D kinematics of the lunge were quantified using the three hip joint centre estimation techniques. Repeated measures ANOVAs were used to compare the discrete 3-D kinematic parameters, and intra-class correlations were employed to identify similarity across the 3-D kinematic waveforms from the three techniques. Results. The results indicate that whilst the kinematic waveforms were similar (R2 ≥0.96); significant differences in discrete parameters were also evident at both the hip and knee joint in the coronal and transverse planes. Conclusions. It appears based on these observations that different hip joint centre locations can significantly influence the resultant kinematic parameters and cannot be used interchangeably. [ABSTRACT FROM AUTHOR]

In human movement analysis, accuracy and robustness of the algorithms used to determine the location of centres of rotation from stereophotogrametric data depend mainly on their capacity to deal with the artefacts due to soft tissue deformation (STA). While evaluating these algorithms using a mathematical simulation approach, effectual realizations of STAs are needed. This study aimed at accomplishing this objective by modelling STAs, in twelve selected areas of the thigh, as a linear function of the hip angles, assuming no knee joint movement. The proposed model was calibrated and assessed using ex-vivo experiments. This entailed that only the component of the STA due to skin stretching was accounted for. Photogrammetric data of markers placed on the skin and on hip-bone and femur pins were recorded during passive flexion-extension, ab-adduction, rotation and circumduction of the hip joint. Artefact skin marker displacements were represented in a femur embedded anatomical frame. Model parameters were estimated by minimizing the least squares difference between measured and modelled STAs. The STA affecting a skin marker placed in a given thigh location of a given subject could be modelled with a high accuracy (median root mean square difference over 4 subjects x 3 trials x 12 markers x 3 coordinates: 0.8 mm--inter quartile range 1.0 mm). This was also true for a hip joint movement different from the one used to calibrate the model. High inter-subject variability of the model parameters confirmed the subject-dependency of the phenomenon. [ABSTRACT FROM AUTHOR]

In morphological analysis of the femur, the hip joint centre (HJC) is generally determined using a 3D model of the femoral head based on medical images. However, the portion of the image selected to represent the femoral head may influence the HJC. We determined if this influence invalidates the results of three HJC calculation methods, one of which we introduce here. To isolate femoral heads in cadaver CT images, thresholds were applied to the distance between femur and acetabulum models. The sensitivity of the HJC to these thresholds and the differences between methods were quantified. For thresholds between 6 and 9 mm and healthy hips, differences between methods were below 1 mm and all methods were insensitive to threshold changes. For higher thresholds, the fovea capitis femoris disturbed the HJC. In two deformed hips, the new method performed superiorly. We conclude that for normal hips all methods produce valid results. [ABSTRACT FROM AUTHOR]

Abstract: Hip loading affects the development of hip osteoarthritis, bone remodelling and osseointegration of implants. In this study, we analyzed the effect of subject-specific modelling of hip geometry and hip joint centre (HJC) location on the quantification of hip joint moments, muscle moments and hip contact forces during gait, using musculoskeletal modelling, inverse dynamic analysis and static optimization. For 10 subjects, hip joint moments, muscle moments and hip loading in terms of magnitude and orientation were quantified using three different model types, each including a different amount of subject-specific detail: (1) a generic scaled musculoskeletal model, (2) a generic scaled musculoskeletal model with subject-specific hip geometry (femoral anteversion, neck-length and neck-shaft angle) and (3) a generic scaled musculoskeletal model with subject-specific hip geometry including HJC location. Subject-specific geometry and HJC location were derived from CT. Significant differences were found between the three model types in HJC location, hip flexion–extension moment and inclination angle of the total contact force in the frontal plane. No model agreement was found between the three model types for the calculation of contact forces in terms of magnitude and orientations, and muscle moments. Therefore, we suggest that personalized models with individualized hip joint geometry and HJC location should be used for the quantification of hip loading. For biomechanical analyses aiming to understand modified hip joint loading, and planning hip surgery in patients with osteoarthritis, the amount of subject-specific detail, related to bone geometry and joint centre location in the musculoskeletal models used, needs to be considered. [Copyright &y& Elsevier]

Abstract: The human hip joint is normally represented as a spherical hinge and its centre of rotation is used to construct femoral anatomical axes and to calculate hip joint moments. The estimate of the hip joint centre (HJC) position using a functional approach is affected by stereophotogrammetric errors and soft tissue artefacts. The aims of this study were (1) to assess the accuracy with which the HJC position can be located using stereophotogrammetry and (2) to investigate the effects of hip motion amplitude on this accuracy. Experiments were conducted on four adult cadavers. Cortical pins, each equipped with a marker cluster, were implanted in the pelvis and femur, and eight skin markers were attached to the thigh. Recordings were made while an operator rotated the hip joint exploiting the widest possible range of motion. For HJC determination, a proximal and a distal thigh skin marker cluster and two recent analytical methods, the quartic sphere fit (QFS) method and the symmetrical centre of rotation estimation (SCoRE) method, were used. Results showed that, when only stereophotogrammetric errors were taken into account, the analytical methods performed equally well. In presence of soft tissue artefacts, HJC errors highly varied among subjects, methods, and skin marker clusters (between 1.4 and 38.5mm). As expected, larger errors were found in the subject with larger soft tissue artefacts. The QFS method and the distal cluster performed generally better and showed a mean HJC location accuracy better than 10mm over all subjects. The analysis on the effect of hip movement amplitude revealed that a reduction of the amplitude does not improve the HJC location accuracy despite a decrease of the artefact amplitude. [Copyright &y& Elsevier]

Abstract: To estimate hip joint angles during selected motor tasks using stereophotogrammetric data, it is necessary to determine the hip joint centre position. The question is whether the errors affecting that determination propagate less to the angles estimates when a three degrees of freedom (DOFs) constraint (spherical hinge) is used between femur and pelvis, rather than when the two bones are assumed to be unconstrained (six DOFs). An analytical relationship between the hip joint centre location error and the joint angle error was obtained limited to the planar case. In the 3-D case, a similar relationship was obtained using a simulation approach based on experimental data. The joint angle patterns resulted in a larger distortion using a constrained approach, especially when wider rotations occur. The range of motion of the hip flexion–extension, obtained simulating different location errors and without taking into account soft tissue artefacts, varied approximately 7deg using a constrained approach and up to 1deg when calculated with an unconstrained approach. Thus, the unconstrained approach should be preferred even though its estimated three linear DOFs most unlikely carry meaningful information. [Copyright &y& Elsevier]

Abstract: Determination of the hip joint centre (HJC) using a functional approach requires access to the kinematics of various body postures. The present study aimed to determine the combined impact of the nature of the movement, its type and the number of cycles, on the accuracy of HJC estimation. Kinematics noise was modelled based on the deformation of hip and thigh clusters of seven subjects, while perfect ball-and-socket movements (used as reference) were calculated based on the movements of one of the subjects. The noise added to the reference kinematics allowed the simulation of 27 tests. Errors were defined as the Euclidean distance between the estimated and the reference HJC. A nested ANOVA and a multiple comparison procedures were performed on all errors. A test including 10 cycles of three different types of limited movements (flexion-extension, abduction–adduction and circumduction) yielded the greatest accuracy for estimating HJC ( mm). Combining different types of movements allowed improving the accuracy. Given that noise increases as a function of the range of a motion, limited movements proved to be the most accurate; however, 10 cycles were required to achieve such results. For trials involving a single cycle, a large movement proved more efficient. [Copyright &y& Elsevier]

Abstract: The location of the hip joint centre (HJC) is required for calculations of hip moments, the location and orientation of the femur, and muscle lengths and lever arms. In clinical gait analysis, the HJC is normally estimated using regression equations based on normative data obtained from adult populations. There is limited relevant anthropometric data available for children, despite the fact that clinical gait analysis is predominantly used for the assessment of children with cerebral palsy. In this study, pelvic MRI scans were taken of eight adults (ages 23–40), 14 healthy children (ages 5–13) and 10 children with spastic diplegic cerebral palsy (ages 6–13). Relevant anatomical landmarks were located in the scans, and the HJC location in pelvic coordinates was found by fitting a sphere to points identified on the femoral head. The predictions of three common regression equations for HJC location were compared to those found directly from MRI. Maximum absolute errors of 31mm were found in adults, 26mm in children, and 31mm in the cerebral palsy group. Results from regression analysis and leave-one-out cross-validation techniques on the MRI data suggested that the best predictors of HJC location were: pelvic depth for the antero-posterior direction; pelvic width and leg length for the supero-inferior direction; and pelvic depth and pelvic width for the medio-lateral direction. For single-variable regression, the exclusion of leg length and pelvic depth from the latter two regression equations is proposed. Regression equations could be generalised across adults, children and the cerebral palsy group. [Copyright &y& Elsevier]

The article presents a study on the location of hip joint centre (HJC) from anatomical landmarks (ALs) towards the reconstruction of automotive seated posture. It mentions the difficulty of measuring the centre for the said posture in an automotive environment due to the lack of such landmarks. It also notes the estimation of its location through regression and functional methods.

Abstract: This paper describes the calibration process for 3-D free hand ultrasound (3-DUS). The Cambridge calibration technique was determined to produce the most repeatable and accurate results when determining the location of a reference object within a water bath. This note additionally validates 3-DUS as a potential measurement tool for the determination of the hip joint centre. It shows that 3-DUS can accurately determine the inter-HJC distance to within 4±2mm. This indicates potential for 3-DUS to be used as a gold standard measurement tool in the identification of the hip joint centre. [Copyright &y& Elsevier]

Joint kinematics can be calculated by Direct Kinematics (DK), which is used in most clinical gait laboratories, or Inverse Kinematics (IK), which is mainly used for musculoskeletal research. In both approaches, joint centre locations are required to compute joint angles. The hip joint centre (HJC) in DK models can be estimated using predictive or functional methods, while in IK models can be obtained by scaling generic models. The aim of the current study was to systematically investigate the impact of HJC location errors on lower limb joint kinematics of a clinical population using DK and IK approaches. Subject-specific kinematic models of eight children with cerebral palsy were built from magnetic resonance images and used as reference models. HJC was then perturbed in 6mm steps within a 60mm cubic grid, and kinematic waveforms were calculated for the reference and perturbed models. HJC perturbations affected only hip and knee joint kinematics in a DK framework, but all joint angles were affected when using IK. In the DK model, joint constraints increased the sensitivity of joint range-of-motion to HJC location errors. Mean joint angle offsets larger than 5° were observed for both approaches (DK and IK), which were larger than previously reported for healthy adults. In the absence of medical images to identify the HJC, predictive or functional methods with small errors in anterior-posterior and medial-lateral directions and scaling procedures minimizing HJC location errors in the anterior-posterior direction should be chosen to minimize the impact on joint kinematics., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)