Reliability of functional and predictive methods to estimate the hip joint centre in human motion analysis in healthy adults

Kainz, Hans; M, Hájek; Modenese, Luca; Saxby, David J.; Lloyd, David G.; Carty, Christopher P.

doi:10.1016/j.gaitpost.2017.01.023

Cited by 28 publications

(20 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The joint centres for the ground-truth model were recovered using functional methods (hip and shoulder), and marker-based methods (ankle, knee, and L5/S1). Geometric sphere fitting for the hip was chosen based on the recommendation by the ISB [40] and as all subjects were able to move sufficiently [41]. The inter-malleolar point was selected for the ankles from Wu [40], the inter-epicondyle point for the knee [42], and L5/S1 from the allometric model described in Section II-E.…”

Section: B Active Motion Capture Modelmentioning

confidence: 99%

Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies

Matthew

Seko

Bajcsy

et al. 2019

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

The study of joint kinematics and dynamics has broad clinical applications including the identification of pathological motions or compensation strategies and the analysis of dynamic stability. High-end motion capture systems, however, are expensive and require dedicated camera spaces with lengthy setup and data processing commitments. Depth cameras, such as the Microsoft Kinect, provide an inexpensive, marker-free alternative at the sacrifice of joint-position accuracy. In this work, we present a fast framework for adding biomechanical constraints to the joint estimates provided by a depth camera system. We also present a new model for the lower lumbar joint angle. We validate key joint position, angle, and velocity measurements against a gold standard active motion-capture system on ten healthy subjects performing sit-to-stand (STS). Our method showed significant improvement in Mean Absolute Error and Intraclass Correlation Coefficients for the recovered joint angles and position-based metrics. These improvements suggest that depth cameras can provide an accurate and clinically viable method of rapidly assessing the kinematics and kinetics of the STS action, providing data for further analysis using biomechanical or machine learning methods.

show abstract

Section: B Active Motion Capture Modelmentioning

confidence: 99%

Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies

Matthew

Seko

Bajcsy

et al. 2019

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…The dimensions of each segment in the model were scaled so that the position of the model markers matched as best as possible the position of the experimental markers. 37 Inverse kinematics and inverse dynamics, from the experimental marker trajectories and ground reaction forces, were used to calculate joint angles and moments. A dynamic musculoskeletal simulation of one gait cycle was generated through the use of the residual reduction algorithm to reduce the non-physiological forces and moments; the use of computed muscle control algorithm 38 to estimate the muscle forces during walking; and then the use of the ''Muscle Force Direction'' plugin 39 to extract the action muscle direction and muscle origin or insertion patches from the model, in accordance with other implementations of the same model.…”

Section: Generic Musculoskeletal Modelmentioning

confidence: 99%

A subject-specific integrative biomechanical framework of the pelvis for gait analysis

Ravera

Crespo

Formento

2018

Proc Inst Mech Eng H

View full text Add to dashboard Cite

Analysis of the human locomotor system using rigid-body musculoskeletal models has increased in the biomechanical community with the objective of studying muscle activations of different movements. Simultaneously, the finite element method has emerged as a complementary approach for analyzing the mechanical behavior of tissues. This study presents an integrative biomechanical framework for gait analysis by linking a musculoskeletal model and a subject-specific finite element model of the pelvis. To investigate its performance, a convergence study was performed and its sensitivity to the use of non-subject-specific material properties was studied. The total hip joint force estimated by the rigid musculoskeletal model and by the finite element model showed good agreement, suggesting that the integrative approach estimates adequately (in shape and magnitude) the hip total contact force. Previous studies found movements of up to 1.4 mm in the anterior-posterior direction, for single leg stance. These results are comparable with the displacement values found in this study: 0-0.5 mm in the sagittal axis. Maximum von Mises stress values of approximately 17 MPa were found in the pelvic bone. Comparing this results with a previous study of our group, the new findings show that the introduction of muscular boundary conditions and the flexion-extension movement of the hip reduce the regions of high stress and distributes more uniformly the stress across the pelvic bone. Thus, it is thought that muscle force has a relevant impact in reducing stresses in pelvic bone during walking of the finite element model proposed in this study. Future work will focus on including other deformable structures, such as the femur and the tibia, and subject-specific material properties.

show abstract

“…Both functional-and regression-based approaches demonstrate average HJC location errors of 20-30mm (Bell et al, 1989;Davis III et al, 1991;Harrington et al, 2007). Errors from regression equations stem from a combination of marker placement error and limitations of the regression equation in approximating the true variation in the population (Schwartz and Rozumalski, 2005), whilst errors from functional methods stem from limited ROM, functional trial performance and soft tissue artefact (Fiorentino et al, 2017;Kainz et al, 2017b;Piazza et al, 2001). Consequently, these methods are not appropriate for individuals who are obese, have hip pain or are elderly.…”

Section: Introductionmentioning

confidence: 99%

Statistical shape modelling versus linear scaling: Effects on predictions of hip joint centre location and muscle moment arms in people with hip osteoarthritis

Bahl

Killen

et al. 2019

Journal of Biomechanics

Self Cite

View full text Add to dashboard Cite

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 ± 3.3mm) compared to functional (36.9 ± 17.5mm, p=<0.001) and regression (31.2 ± 15mm, 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 ± 3.1mm, p=0.002). Average RMSE were greatest for the hip flexor and extensor muscle groups using the functional (16.71mm and 8.87mm respectively) and regression methods (16.15mm and 9.97mm respectively). The effects on moment-arms were less substantial for the shape modelling methods, ranging from 0.05 to 3.2mm. Shape modelling methods improved HJC location and muscle moment-arm estimates compared to linear scaling of musculoskeletal models in patients with hip osteoarthritis.

show abstract

Reliability of functional and predictive methods to estimate the hip joint centre in human motion analysis in healthy adults

Cited by 28 publications

References 30 publications

Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies

Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies

A subject-specific integrative biomechanical framework of the pelvis for gait analysis

Statistical shape modelling versus linear scaling: Effects on predictions of hip joint centre location and muscle moment arms in people with hip osteoarthritis

Contact Info

Product

Resources

About