Computational Modeling of a Dynamic Knee Simulator for Reproduction of Knee Loading

2005 ◽  
Vol 127 (7) ◽  
pp. 1216-1221 ◽  
Author(s):  
Trent M. Guess ◽  
Lorin P. Maletsky
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Sagittal Plane ◽  
Three Dimensional ◽  
Joint Loading ◽  
Load Prediction ◽  
Prosthetic Knee ◽  
Knee Simulator ◽  
Knee Loading ◽  
Future Work

As a first step towards reproducing desired three-dimensional joint loading and motion on a dynamic knee simulator, the goal of this study was to develop and verify a three-dimensional computational model that generated control profiles for the simulator using desired knee loading and motion as model inputs. The developed model was verified by predicting tibio-femoral loading on an instrumented analog knee for given actuator forces and the ability to generate simulator control profiles was demonstrated using a three-dimensional walking profile. The model predicted axial tibia loading for a sagittal-plane dual-limb squat within 1% of measured peak loading. Adding out-of-sagittal-plane forces decreased the accuracy of load prediction. The model generated control profiles to the simulator that produced axial tibia loading within 16% of desired for walking. Discrepancies in predicted and measured quadriceps forces influenced the accuracy of the generated control profiles. Future work will replace the analog knee in both the model and machine with a prosthetic knee.

Computational Modeling of a Dynamic Knee Simulator for Reproduction of Joint Loading

2003 ◽  
Author(s):  
Trent M. Guess ◽  
Lorin P. Maletsky
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Three Dimensional ◽  
Joint Loading ◽  
Knee Simulator ◽  
Joint Forces ◽  
Five Axis ◽  
Five Axes

A three-dimensional dynamic computational model was developed for the dual purposes of predicting and replicating joint loading generated by a five-axis dynamic knee simulator. The model was verified through an analog knee that was constrained for accurate modeling and instrumented to directly measure joint forces. The verified model was then used to generate control profiles to the five axes of the simulator for replication of desired joint loading. Reproduction of a walking profile is demonstrated.

scholarly journals Verification of a Dynamic Knee Simulator Computational Model

2008 ◽  
Author(s):  
Amber N. Reeve ◽  
Mike A. Strickland ◽  
Lorin P. Maletsky ◽  
Mark Taylor
Keyword(s):  
Computational Model ◽  
Sagittal Plane ◽  
Human Motion ◽  
Joint Friction ◽  
Knee Simulator ◽  
Physiological Loading ◽  
Knee Loading ◽  
Out Of Plane ◽  
Multi Body ◽  
Dynamics Models

Dynamic knee simulators aim to reproduce prescribed physiological loading and motions of the knee. The natural knee achieves stability through a complex interaction of the neuro-musculoskeletal system; thus, a knee simulator also requires a sophisticated control system to replicate human motion. Guess and Maletsky [1] developed a computational model to predict the required simulator inputs to produce the desired knee loading for dynamic activities on the Kansas Knee Simulator (KKS). The model built demonstrated conceptually that multi-body dynamics models could be used to simulate the KKS. However, as desired loading profiles became more complex, key limitations were discovered in the model; such as the model controller limited to a single axis under feedback control, no out-of-plane loading, not accounting for dynamic joint friction or damping of the actuators, and an inability of the model to flex pass 80° degrees of knee flexion. Thus, there was a need for a new computational model to overcome the limitations and to provide a more robust and complete comparison to the KKS. The new computational model will allow better utilization of the KKS capabilities for future cadaveric and prosthetic testing. This work outlines the sagittal-plane validation of the new computational model.

ARE TWO-DIMENSIONAL MEASURED SAGITTAL PLANE KINEMATICS RELATED TO THREE-DIMENSIONAL JOINT LOADING DURING THE DROP VERTICAL JUMP TEST?

2014 ◽  
Vol 48 (7) ◽  
pp. 585.2-585 ◽  
Author(s):  
B Dingenen ◽  
B Malfait ◽  
J Vanrenterghem ◽  
M Robinson ◽  
S Verschueren ◽  
...  
Keyword(s):  
Sagittal Plane ◽  
Vertical Jump ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Joint Loading ◽  
Jump Test ◽  
Drop Vertical Jump

Computational Modeling of Finger Swipe Gestures on Touchscreen

2016 ◽  
Vol 60 (1) ◽  
pp. 1721-1725 ◽  
Author(s):  
Heejin Jeong ◽  
Yili Liu
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Human Subject ◽  
Movement Time ◽  
Cognitive Architecture ◽  
Three Dimensional ◽  
Finger Movement ◽  
Queuing Network ◽  
3D Space ◽  
Model Human Processor

Although swiping (also called flicking) is one of the commonly used touchscreen gestures, few modeling studies have been conducted. In this paper, a computational model that focuses on touchscreen swipe gestures was developed by extending the QN-MHP (Queuing Network-Model Human Processor) architecture. The model assumed that the swiped-route follows a three-dimensional path. To model the finger swipe gesture, an operator (i.e., “ Swipe-with-finger”) for the Queuing Network Cognitive Architecture was developed using an existing regression equation for predicting the finger movement time in 3D space (Cha and Myung, 2013). The model was validated with two corresponding experimental results in the literature. As a result, the swiping times generated by the model were well fit with the human subject data.

scholarly journals How Computational Modeling Can Force Theory Building in Psychological Science

2021 ◽  
pp. 174569162097058
Author(s):  
Olivia Guest ◽  
Andrea E. Martin
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Process Modeling ◽  
Theory Building ◽  
Formal Modeling ◽  
Inference Process ◽  
Experimental Practice ◽  
Psychological Science ◽  
Scientific Inference ◽  
And Behaviors

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

A Computational Analysis of Flow Field in Twin-Track Subway Tunnel to Improve Air Quality

2013 ◽  
Vol 319 ◽  
pp. 599-604
Author(s):  
Makhsuda Juraeva ◽  
Kyung Jin Ryu ◽  
Sang Hyun Jeong ◽  
Dong Joo Song
Keyword(s):  
Air Quality ◽  
Computational Model ◽  
Stokes Equations ◽  
Ventilation System ◽  
Three Dimensional ◽  
Aerodynamic Characteristics ◽  
Navier Stokes ◽  
Subway Tunnel ◽  
Air Supply ◽  
And Performance

A computational model of existing Seoul subway tunnelwas analyzed in this research. The computational model was comprised of one natural ventilationshaft, two mechanical ventilationshafts, one mechanical airsupply, a twin-track tunnel, and a train. Understanding the flow pattern of the train-induced airflow in the tunnel was necessary to improve ventilation performance. The research objective wasto improve the air quality in the tunnel by investigating train-induced airflow in the twin-track subway tunnel numerically. The numerical analysis characterized the aerodynamic behavior and performance of the ventilation system by solving three-dimensional turbulent Reynolds-averaged Navier-Stokes equations. ANSYS CFX software was used for the computations. The ventilation and aerodynamic characteristics in the tunnel were investigated by analyzing the mass flowrateat the exits of the ventilation mechanicalshafts. As the train passed the mechanical ventilation shafts, the amount of discharged-air in the ventilationshafts decreased rapidly. The air at the exits of the ventilation shafts was gradually recovered with time, after the train passed the ventilation shafts. The developed mechanical air-supply for discharging dusty air and supplying clean airwas investigated.The computational results showed that the developed mechanical air-supplycould improve the air quality in the tunnel.

scholarly journals Computational Model for Cell Migration in Three-Dimensional Matrices

2005 ◽  
Vol 89 (2) ◽  
pp. 1389-1397 ◽  
Author(s):  
Muhammad H. Zaman ◽  
Roger D. Kamm ◽  
Paul Matsudaira ◽  
Douglas A. Lauffenburger
Keyword(s):  
Cell Migration ◽  
Computational Model ◽  
Three Dimensional

scholarly journals Modelling human skull growth: a validated computational model

2017 ◽  
Vol 14 (130) ◽  
pp. 20170202 ◽  
Author(s):  
Joseph Libby ◽  
Arsalan Marghoub ◽  
David Johnson ◽  
Roman H. Khonsari ◽  
Michael J. Fagan ◽  
...  
Keyword(s):  
Computational Model ◽  
Three Dimensional ◽  
Basic Knowledge ◽  
Fe Model ◽  
Adult Size ◽  
Fe Method ◽  
Percentage Difference ◽  
Skull Growth

During the first year of life, the brain grows rapidly and the neurocranium increases to about 65% of its adult size. Our understanding of the relationship between the biomechanical forces, especially from the growing brain, the craniofacial soft tissue structures and the individual bone plates of the skull vault is still limited. This basic knowledge could help in the future planning of craniofacial surgical operations. The aim of this study was to develop a validated computational model of skull growth, based on the finite-element (FE) method, to help understand the biomechanics of skull growth. To do this, a two-step validation study was carried out. First, an in vitro physical three-dimensional printed model and an in silico FE model were created from the same micro-CT scan of an infant skull and loaded with forces from the growing brain from zero to two months of age. The results from the in vitro model validated the FE model before it was further developed to expand from 0 to 12 months of age. This second FE model was compared directly with in vivo clinical CT scans of infants without craniofacial conditions ( n = 56). The various models were compared in terms of predicted skull width, length and circumference, while the overall shape was quantified using three-dimensional distance plots. Statistical analysis yielded no significant differences between the male skull models. All size measurements from the FE model versus the in vitro physical model were within 5%, with one exception showing a 7.6% difference. The FE model and in vivo data also correlated well, with the largest percentage difference in size being 8.3%. Overall, the FE model results matched well with both the in vitro and in vivo data. With further development and model refinement, this modelling method could be used to assist in preoperative planning of craniofacial surgery procedures and could help to reduce reoperation rates.

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