Which method of hip joint centre localisation should be used in gait analysis?

Accuracy of Functional and Predictive Methods to Calculate the Hip Joint Center in Young Non-pathologic Asymptomatic Adults with Dual Fluoroscopy as a Reference Standard

Predictions from biomechanical models of gait may be sensitive to joint center locations. Most often, the hip joint center (HJC) is derived from locations of reflective markers adhered to the skin. Here, predictive techniques use regression equations of pelvic anatomy to estimate the HJC, whereas functional methods track motion of markers placed at the pelvis and femur during a coordinated motion. Skin motion artifact may introduce errors in the estimate of HJC for both techniques. Quantifying the accuracy of these methods is an area of open investigation. In this study, we used dual fluoroscopy (DF) (a dynamic X-ray imaging technique) and three-dimensional reconstructions from computed tomography images, to measure HJC locations in vivo. Using dual fluoroscopy as the reference standard, we then assessed the accuracy of three predictive and two functional methods. Eleven non-pathologic subjects were imaged with DF and reflective skin marker motion capture. Additionally, DF-based solutions generated virtual markers placed on bony landmarks, which were input to the predictive and functional methods to determine if estimates of the HJC improved. Using skin markers, functional methods had better mean agreement with the HJC measured by DF (11.0 ± 3.3 mm) than predictive methods (18.1 ± 9.5 mm); estimates from functional and predictive methods improved when using the DF-based solutions (1.3 ± 0.9 and 17.5 ± 8.6 mm, respectively). The Harrington method was the best predictive technique using both skin markers (13.2 ± 6.5 mm) and DF-based solutions (10.6 ± 2.5 mm). The two functional methods had similar accuracy using skin makers (11.1 ± 3.6 and 10.8 ± 3.2 mm) and DF-based solutions (1.2 ± 0.8 and 1.4 ± 1.0 mm). Overall, functional methods were superior to predictive methods for HJC estimation. However, the improvements observed when using the DF-based solutions suggest that skin motion artifact is a large source of error for the functional methods.

On the implementation of predictive methods to locate the hip joint centres

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.

Estimation of the hip joint centre in human motion analysis: a systematic review

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.

The clinical impact of hip joint centre regression equation error on kinematics and kinetics during paediatric gait

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.

Cross-talk correction method for knee kinematics in gait analysis using principal component analysis (PCA): a new proposal

Background

In 3D gait analysis, the knee joint is usually described by the Eulerian way. It consists in breaking down the motion between the articulating bones of the knee into three rotations around three axes: flexion/extension, abduction/adduction and internal/external rotation. However, the definition of these axes is prone to error, such as the “cross-talk” effect, due to difficult positioning of anatomical landmarks. This paper proposes a correction method, principal component analysis (PCA), based on an objective kinematic criterion for standardization, in order to improve knee joint kinematic analysis.

Methods

The method was applied to the 3D gait data of two different groups (twenty healthy subjects and four with knee osteoarthritis). Then, this method was evaluated with respect to three main criteria: (1) the deletion of knee joint angle cross-talk (2) the reduction of variance in the varus/valgus kinematic profile (3) the posture trial varus/valgus deformation matching the X-ray value for patients with knee osteoarthritis. The effect of the correction method was tested statistically on variabilities and cross-talk during gait.

Results

Cross-talk was lower (p<0.05) after correction (the correlation between the flexion-extension and varus-valgus kinematic profiles being annihilated). Additionally, the variance in the kinematic profile for knee varus/valgus and knee flexion/extension was found to be lower and higher (p<0.05), respectively, after correction for both the left and right side. Moreover, after correction, the posture trial varus/valgus angles were much closer to x-ray grading.

Conclusion

The results show that the PCA correction applied to the knee joint eliminates the cross-talk effect, and does not alter the radiological varus/valgus deformation for patients with knee osteoarthritis. These findings suggest that the proposed correction method produces new rotational axes that better fit true knee motion.