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Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_4 | Pages 13 - 13
1 Apr 2018
Van Houcke J Galibarov PE Fauconnier S Pattyn C Audenaert EE
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Introduction

A deep squat (DS) is a challenging motion at the level of the hip joint generating substantial reaction forces (HJRF). During DS, the hip flexion angle approximates the functional range of hip motion. In some hip morphologies this femoroacetabular conflict has been shown to occur as early as 80° of hip flexion. So far in-vivo HJRF measurements have been limited to instrumented hip implants in a limited number of older patients performing incomplete squats (< 50° hip flexion and < 80° knee flexion). Clearly, young adults have a different kinetical profile with hip and knee flexion ranges going well over 100 degrees. Since hip loading data on this subgroup of the population is lacking and performing invasive measurements would be unfeasible, this study aimed to report a personalised numerical model solution based on inverse dynamics to calculate realistic in silico HJRF values during DS.

M&M

Fifty athletic males (18–25 years old) were prospectively recruited for motion and morphological analysis. DS motion capture (MoCap) acquisitions and MRI scans of the lower extremities 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 nonlinear morphing. Furthermore, 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 squat motion by resolving an overdeterminate kinematics problem. Inverse dynamics analyses were carried out to compute muscle and joint reaction forces in the entire body. Resulting hip joint loads were validated with measured in-vivo data from Knee bend trials in the OrthoLoad library. Additionally, anterior pelvic tilt, hip and knee joint angles were computed.


Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_6 | Pages 62 - 62
1 Apr 2018
Van Houcke J Galibarov P Allaert E Pattyn C Audenaert E
Full Access

Introduction

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.

Material and methods

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.