Effective stiffness, damping and mass of the body during laboratory simulations of shoulder checks in ice hockey

Aguiar, Olivia M G; Radivojevic, Olga; Potvin, Brigitte M.; Vakili, Omid; Robinovitch, Stephen N.

doi:10.1080/14763141.2021.1951828

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…However, for studies with human subjects, the translation of external forces through the body to specific structures (femur, spine, wrist, etc) can only be approximated but not directly calculated. Several groups have employed a range of computational approaches in trying to determine internal tissue loading corresponding to fall events including finite element models, 53 , 54 spring-mass-damper lumped force models, 55 and rigid body dynamic simulations. 18 , 30 , 56 Each approach provides insights into tissue loading and injury risk; however, we are becoming increasingly aware of the need for these models to capture individual variability instead of a single generic computational model to accurately capture tissue mechanics.…”

Section: Discussionmentioning

confidence: 99%

Impact forces in backward falls: Subject-specific video-based rigid body simulation of backward falls

Khorami,

Obaid,

Bhatnagar

et al. 2023

Proc Inst Mech Eng H

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A critical missing component in the study of real-world falls is the ability to accurately determine impact forces resulting from the fall. Subject-specific rigid body dynamic (RBD) models calibrated to video captured falls can quantify impact forces and provide additional insights into injury risk factors. RBD models were developed based on five backward falls captured on surveillance video in long-term care facilities in British Columbia, Canada. Model joint stiffness and initial velocities were calibrated to match the kinematics of the fall and contact forces were calculated. The effect of joint stiffnesses (neck, lumbar spine, hip, and knee joint) on head contact forces were determined by modifying the calibrated stiffness values ±25%. Fall duration, fall trajectories, and maximum velocities showed a close match between fall events and simulations. The maximum value of pelvic velocity difference between Kinovea (an open-source software 2D digitization software) and Madymo multibody modeling was found to be 6% ± 21.58%. Our results demonstrate that neck and hip stiffness values have a non-significant yet large effect on head contact force ( t(3) = 1, p = 0.387 and t(3) = 2, p = 0.138), while lower effects were observed for knee stiffness, and the effect of lumbar spine stiffness was negligible. The subject-specific fall simulations constructed from real world video captured falls allow for direct quantification of force outcomes of falls and may have applications in improving the assessment of fall-induced injury risks and injury prevention methods.

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Section: Discussionmentioning

confidence: 99%

Impact forces in backward falls: Subject-specific video-based rigid body simulation of backward falls

Khorami,

Obaid,

Bhatnagar

et al. 2023

Proc Inst Mech Eng H

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Shoulder injuries in ice hockey players: Prevalence, common management, and return to play

White

OʼConnor

Sestak

et al. 2023

Journal of Orthopaedics

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Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power

Mayerhofer

Bajić

Donelan

2023

Preprint

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Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles, or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled.

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Effective stiffness, damping and mass of the body during laboratory simulations of shoulder checks in ice hockey

Cited by 3 publications

References 30 publications

Impact forces in backward falls: Subject-specific video-based rigid body simulation of backward falls

Impact forces in backward falls: Subject-specific video-based rigid body simulation of backward falls

Shoulder injuries in ice hockey players: Prevalence, common management, and return to play

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power

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