A deep squat (DS) is a challenging motion at the level of the hip joint generating substantial reaction forces (HJRF). As a closed chain exercise, it has great value in rehabilitation and muscle strengthening of hip and knee. During DS, the hip flexion angle approximates the functional range of hip motion risking femoroacetabular impingement in some morphologies. In-vivo HJRF measurements have been limited to instrumented implants in a limited number of older patients performing incomplete squats (< 50° hip flexion and < 80° knee flexion). On the other hand, total hip arthroplasty is being increasingly performed in a younger and higher demanding patient population. These patients clearly have a different kinetical profile with hip and knee flexion ranges going well over 100 degrees. Since measurements of HJRF with instrumented prostheses in healthy subjects would be ethically unfeasible, this study aims to report a personalised numerical solution based on inverse dynamics to calculate realistic in-silico HJRF values during DS. Thirty-five healthy males (18–25 years old) were prospectively recruited for motion and morphological analysis. DS motion capture (MoCap) acquisitions and MRI scans with gait lab marker positions were obtained. The AnyBody Modelling System (v6.1.1) was used to implement a novel personalisation workflow of the AnyMoCap template model. Bone geometries, semi-automatically segmented from MRI, and corresponding markers were incorporated into the template human model by an automated procedure. A state of-the-art TLEM 2.0 dataset, included in the Anybody Managed Model Repository (v2.0), was used in the template model. The subject-specific MoCap trials were processed to compute kinematics of DS, muscle and joint reaction forces in the entire body. Resulting hip joint loads were compared with in-vivo data from OrthoLoad dataset. Additionally, hip and knee joint angles were computed.Introduction
Material and methods
Computational modelling has the potential of becoming a useful tool in assessing revision risk on a patient-specific basis. However, there are many difficulties encountered in generating subject-specific computational models that have unknown influences on such predictions, e.g. accuracy of the anatomical geometry and material properties of the patient. This study compares the influence of these two patient-specific parameters on predictions of revision risk due to aseptic loosening. First, X-rays from seventeen patients were processed using previously developed technique utilising rigid scaling of a generic femur to match selected dimensions from each patient’s post-operative X-ray and, then, the same set of 3D models was obtained by using an automated technique that generates 3D extra-cortical geometries from planar X-rays using a combination of 2D contour extraction and 3D warping of a generic model to match the extracted contour. A cement and cement-metal interfacial damage accumulation algorithm developed previously was used. For each geometric set two types of simulations were performed. First, constant cortical and cancellous bone apparent Young’s moduli were assumed. A second set of simulations used age-dependent Young’s moduli for each bone type. Walking and stair-climbing activities were simulated. Resultant migration of the prostheses was used to indicate revision risk. Factorial analysis has shown that the geometry has a larger influence on resultant migration magnitude for each case; however, unexpectedly, using more realistic geometry weakened the strength of predictions. This is most likely to be due ongoing mesh-induced contact problems.
