Population-Based Bone Strain During Physical Activity: A Novel Method Demonstrated for the Human Femur

Ziaeipoor, Hamed; Taylor, Mark; Martelli, Saulo

doi:10.1007/s10439-020-02483-3

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Similarly, bone anatomy and distribution can be obtained from calibrated CT images [10••, 22, 31] or extracted from population databases [76]. Access to high performance computing hardware, efficient new computational algorithms [74,75,80,81] and open-source population databases [71] have reduced Fig. 4 The hip load generated by contractions of the gluteus (gluteus medius and minimus) and of the hamstring muscles.…”

Section: A Perspective Toward Personalized Exercise Prescriptionmentioning

confidence: 99%

Modelling Human Locomotion to Inform Exercise Prescription for Osteoporosis

Martelli

Beck

Saxby

et al. 2020

Curr Osteoporos Rep

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Purpose of Review We review the literature on hip fracture mechanics and models of hip strain during exercise to postulate the exercise regimen for best promoting hip strength. Recent Findings The superior neck is a common location for hip fracture and a relevant exercise target for osteoporosis. Current modelling studies showed that fast walking and stair ambulation, but not necessarily running, optimally load the femoral neck and therefore theoretically would mitigate the natural age-related bone decline, being easily integrated into routine daily activity. High intensity jumps and hopping have been shown to promote anabolic response by inducing high strain in the superior anterior neck. Multidirectional exercises may cause beneficial non-habitual strain patterns across the entire femoral neck. Resistance knee flexion and hip extension exercises can induce high strain in the superior neck when performed using maximal resistance loadings in the average population. Summary Exercise can stimulate an anabolic response of the femoral neck either by causing higher than normal bone strain over the entire hip region or by causing bending of the neck and localized strain in the superior cortex. Digital technologies have enabled studying interdependences between anatomy, bone distribution, exercise, strain and metabolism and may soon enable personalized prescription of exercise for optimal hip strength.

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Section: A Perspective Toward Personalized Exercise Prescriptionmentioning

confidence: 99%

Modelling Human Locomotion to Inform Exercise Prescription for Osteoporosis

Martelli

Beck

Saxby

et al. 2020

Curr Osteoporos Rep

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“…For example, the finite element analysis (FEA) can be reduced to a surrogate using statistical interpolation of a meaningful sample of outputsa process often referred to as "Kriging" after statistician Danie Krige. This is relevant because the computational gains of Kriging make FEA outputs such as tissue stresses and strains viable in real-time, as has been demonstrated in impressive fashion recently to understand femur mechanics (Ziaeipoor et al 2019a;Ziaeipoor et al 2020;Ziaeipoor et al 2019b). Real-time capacity is a requirement for future translation to clinical or in-field conditions, where clinicians/coaches/commanders and their patients/athletes/soldiers want immediate feedback about how behavioural choices influence sub-tissue level mechanics.…”

Section: Machine Learning To Accelerate Neuromusculoskeletal Modellingmentioning

confidence: 99%

Machine learning methods to support personalized neuromusculoskeletal modelling

Saxby

Killen

Pizzolato

et al. 2020

Biomech Model Mechanobiol

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Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified and big data/machine learning approaches for implementation are presented together with recommendations for further research.

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