2-D Inverse Dynamics Knee Model: Aligning Anatomical Knee Model With Knee Extension Kinematic Data Using Ligament Forces

2018 ◽  
Author(s):  
Jose Mario Salinas ◽  
Dumitru I. Caruntu
Keyword(s):  
Experimental Data ◽  
Coordinate System ◽  
Inverse Dynamics ◽  
Knee Extension ◽  
The Body ◽  
Body Segment ◽  
Center Of Rotation ◽  
Axis Of Rotation ◽  
Instantaneous Center ◽  
Body Fixed Coordinate System

This paper deals with aligning knee geometrical anatomical data with kinematic data from experimental work in order to develop a two-dimensional inverse dynamics anatomical model of human knee. Motion capture cameras were used to collect the experimental data for a knee extension exercise. Reflective markers were placed on the subjects’ skin during the experiment. In this model, joints such as hip, knee, and ankle are represented by axes of rotation. These axes are determined by calculating the relative instantaneous center of rotation of one body segment with respect to an adjacent body segment. Body-fixed coordinate systems were defined using three reflective markers attached to the subject. The origin of each body fixed-coordinate system was located between the three markers on that body segment, the body-fixed x-axis was pointing towards the marker on the lateral side of the body segment, and the body-fixed y-axis fell on the same plane as the three reflective markers on the body segment. Moreover, the axis of rotation that represents the hip was determined by calculating the instantaneous center of rotation of reflective markers located on the pelvis with respect to a body fixed coordinate system on the thigh. The axis of rotation on the knee was determined by calculating the instantaneous center of rotation of reflective markers on the shin (tibia) with respect to the body-fixed coordinate system on the thigh (femur). The axis of rotation on the ankle was determined by calculating the instantaneous center of rotation of reflective markers on the shin with respect to a body-fixed coordinate system on the foot. Bone anatomical geometries of femur and tibia were represented mathematically as polynomials and superimposed over the experimental data. This was done by matching the center of rotation from experimental data with the geometric center of the femoral condyle. This is necessary for estimating the insertions/origins of knee ligaments. These ligaments are modeled as nonlinear elastic springs. Furthermore, ligaments were divided into separate fiber bundles. Both the posterior and anterior cruciate ligaments were divided into an anterior and posterior fiber bundle. The cruciate ligament forces for both exercises are discussed in this paper.

2-D Inverse Dynamics Knee Model: Aligning Anatomical Knee Model With Squatting Kinematic Data Using Ligament Forces

2018 ◽  
Author(s):  
Jose Mario Salinas ◽  
Dumitru I. Caruntu
Keyword(s):  
Experimental Data ◽  
Inverse Dynamics ◽  
Femoral Condyle ◽  
Cruciate Ligament ◽  
Optimization Technique ◽  
Body Segment ◽  
Kinematic Data ◽  
Center Of Rotation ◽  
Knee Model ◽  
Bone Structures

A 2-dimensional anatomical knee model was developed for aligning knee joint related bone structures with experimental kinematic data. The experimental data was collected using motion capture cameras, which recorded the position of reflective markers placed on the human subject. Velocities were calculated by numerically differentiating the marker position with respect to time. Joints, such as the hip, knee, and ankle, were represented by axes of rotation. These axes were determined by calculating the relative instantaneous center of rotation of a body segment with respect to the adjacent body segment. Body-fixed coordinate systems were set for both thigh and shin. Anatomical bone structures were obtained from an x-ray and represented mathematically as polynomials. The femoral bone surface was aligned with the experimental data by superimposing the center of rotation of the shin with respect to thigh with the geometric center of the femoral condyle. The tibial surface was aligned with the experimental data by aligning the bones at minimum flexion and then superimposing the tibia with a shared point between femur and tibia. Ligaments were modeled as non-linear elastic springs. Cruciate ligaments were divided into a posterior and anterior ligament fiber bundle. Cruciate ligament forces were calculated for the squatting exercise for five different femoral geometric centers. Geometric centers were determined using a nonlinear least squares optimization technique. Cruciate ligament forces are discussed in this paper.

scholarly journals Zur exakten Behandlung von kollektiven Rotationen Teil A: Theorie / On the Exact Treatment of Collective Rotations Part A: Theory

1973 ◽  
Vol 28 (2) ◽  
pp. 206-215
Author(s):  
Hanns Ruder
Keyword(s):  
Coordinate System ◽  
Perturbation Expansion ◽  
Rotational Energy ◽  
The Body ◽  
System A ◽  
N Body Problem ◽  
Definition Of ◽  
Fixed System ◽  
Body Fixed Coordinate System ◽  
Groundstate Energy

Basic in the treatment of collective rotations is the definition of a body-fixed coordinate system. A kinematical method is derived to obtain the Hamiltonian of a n-body problem for a given definition of the body-fixed system. From this exact Hamiltonian, a consequent perturbation expansion in terms of the total angular momentum leads to two exact expressions: one for the collective rotational energy which has to be added to the groundstate energy in this order of perturbation and a second one for the effective inertia tensor in the groundstate. The discussion of these results leads to two criteria how to define the best body-fixed coordinate system, namely a differential equation and a variational principle. The equivalence of both is shown.

An Efficient Probabilistic Methodology for Incorporating Uncertainty in Body Segment Parameters and Anatomical Landmarks in Joint Loadings Estimated From Inverse Dynamics

2008 ◽  
Vol 130 (1) ◽  
Author(s):  
Joseph E. Langenderfer ◽  
Peter J. Laz ◽  
Anthony J. Petrella ◽  
Paul J. Rullkoetter
Keyword(s):  
Lower Extremity ◽  
Inverse Dynamics ◽  
The Body ◽  
Implant Design ◽  
Anatomical Landmarks ◽  
Body Segment ◽  
Joint Loading ◽  
Data Set ◽  
Reaction Data ◽  
Body Segment Parameters

Inverse dynamics is a standard approach for estimating joint loadings in the lower extremity from kinematic and ground reaction data for use in clinical and research gait studies. Variability in estimating body segment parameters and uncertainty in defining anatomical landmarks have the potential to impact predicted joint loading. This study demonstrates the application of efficient probabilistic methods to quantify the effect of uncertainty in these parameters and landmarks on joint loading in an inverse-dynamics model, and identifies the relative importance of the parameters and landmarks to the predicted joint loading. The inverse-dynamics analysis used a benchmark data set of lower-extremity kinematics and ground reaction data during the stance phase of gait to predict the three-dimensional intersegmental forces and moments. The probabilistic analysis predicted the 1–99 percentile ranges of intersegmental forces and moments at the hip, knee, and ankle. Variabilities, in forces and moments of up to 56% and 156% of the mean values were predicted based on coefficients of variation less than 0.20 for the body segment parameters and standard deviations of 2mm for the anatomical landmarks. Sensitivity factors identified the important parameters for the specific joint and component directions. Anatomical landmarks affected moments to a larger extent than body segment parameters. Additionally, for forces, anatomical landmarks had a larger effect than body segment parameters, with the exception of segment masses, which were important to the proximal-distal joint forces. The probabilistic modeling approach predicted the range of possible joint loading, which has implications in gait studies, clinical assessments, and implant design evaluations.

scholarly journals Comparison of Joint and Muscle Biomechanics in Maximal Flywheel Squat and Leg Press

2021 ◽  
Vol 3 ◽  
Author(s):  
Maria Sjöberg ◽  
Hans E. Berg ◽  
Lena Norrbrand ◽  
Michael S. Andersen ◽  
Elena M. Gutierrez-Farewik ◽  
...  
Keyword(s):  
Muscle Activity ◽  
Injury Risk ◽  
Inverse Dynamics ◽  
Reaction Force ◽  
Quadriceps Muscle ◽  
Knee Extension ◽  
The Body ◽  
Exercise Load ◽  
Leg Extension ◽  
Leg Press

The aim was to compare the musculoskeletal load distribution and muscle activity in two types of maximal flywheel leg-extension resistance exercises: horizontal leg press, during which the entire load is external, and squat, during which part of the load comprises the body weight. Nine healthy adult habitually strength-training individuals were investigated. Motion analysis and inverse dynamics-based musculoskeletal modelling were used to compute joint loads, muscle forces, and muscle activities. Total exercise load (resultant ground reaction force; rGRF) and the knee-extension net joint moment (NJM) were slightly and considerably greater, respectively, in squat than in leg press (p ≤ 0.04), whereas the hip-extension NJM was moderately greater in leg press than in squat (p = 0.03). Leg press was performed at 11° deeper knee-flexion angle than squat (p = 0.01). Quadriceps muscle activity was similar in squat and leg press. Both exercise modalities showed slightly to moderately greater force in the vastii muscles during the eccentric than concentric phase of a repetition (p ≤ 0.05), indicating eccentric overload. That the quadriceps muscle activity was similar in squat and leg press, while rGRF and NJM about the knee were greater in squat than leg press, may, together with the finding of a propensity to perform leg press at deeper knee angle than squat, suggest that leg press is the preferable leg-extension resistance exercise, both from a training efficacy and injury risk perspective.

Instantaneous Center of Rotation in Pitch Response of a FPSO Submitted to Head Waves

2018 ◽  
Author(s):  
Daniel de Oliveira Costa ◽  
Antonio Carlos Fernandes ◽  
Joel Sena Sales Junior ◽  
Peyman Asgari
Keyword(s):  
Rigid Body ◽  
Degrees Of Freedom ◽  
Tracking System ◽  
The Body ◽  
Body Motion ◽  
Floating Body ◽  
Wave System ◽  
Center Of Rotation ◽  
Head Waves ◽  
Instantaneous Center

When under influence of an incident wave system, any floating body presents a general motion with all six degrees of freedom, unless it presents some kind of restrains on it. For a free moving body, the center of rotation will depend on the force distribution and might not coincide with its center of gravity. For long and slender floating structures, such as FPSO platforms, a small change in the center of Pitch rotation would result in significant change in the overall motions in its fore and aft regions. Therefore, it is of high importance to obtain a better understating of the instantaneous position of the body center of rotation in Heave and Pitch response. This paper investigates the position of the Instantaneous Center of Rotation in Pitch Response of a scaled down model of a FPSO platform under different regular wave conditions. The investigation uses basic kinematics equations for rigid body, defining the 6 degrees of freedom of the rigid body motion from a finite number of markers installed in the model. A high quality tracking system captures the markers positions in order to define the rigid body at each instant of time. For an initial approach, the study considers the response due to head waves seas with experimental validation.

Instantaneous center/axis of rotation for planar and three-dimensional motion

2021 ◽  
pp. 030641902110511
Author(s):  
Donald L Kunz
Keyword(s):  
Rigid Body ◽  
Closed Form ◽  
Three Dimensional ◽  
The Body ◽  
Body Motion ◽  
Planar Motion ◽  
Instantaneous Axis Of Rotation ◽  
Axis Of Rotation ◽  
Instantaneous Center ◽  
Instantaneous Axis

This article discusses a direct analytical method for calculating the instantaneous center of rotation and the instantaneous axis of rotation for the two-dimensional and three-dimensional motion, respectively, of rigid bodies. In the case of planar motion, this method produces a closed-form expression for the instantaneous center of rotation based on a single point located on the rigid body. It can also be used to derive closed-form expressions for the body and space centrodes. For three-dimensional, rigid body motion, an extension of the technique used for planar motion locates a point on the instantaneous axis of rotation, which is parallel to the body angular velocity vector. In addition, methods are demonstrated that can be used to map the body and space cones for general rigid body motion, and locate the fixed point for the body.

Pose estimation of a fast tumbling space noncooperative target using the time-of-flight camera

2021 ◽  
pp. 095441002110002
Author(s):  
Qi-shuai Wang ◽  
Guo-ping Cai
Keyword(s):  
Coordinate System ◽  
Pose Estimation ◽  
Point Cloud ◽  
Time Of Flight ◽  
Point Clouds ◽  
The Body ◽  
Geometric Characteristics ◽  
Pose Tracking ◽  
Plane Point ◽  
Body Fixed Coordinate System

This article proposes a pose estimation method for a fast tumbling space noncooperative target. The core idea of this method is to extract the target’s body-fixed coordinate system by using the geometric characteristics of the target’s point cloud and then by the body-fixed coordinate system to realize pose initialization and pose tracking of the target. In the extraction of the body-fixed coordinate system, a point cloud of the target, which can be obtained by a time-of-flight camera, can be divided into small plane point clouds firstly; then the geometric information of these plane point clouds can be utilized to extract the target’s descriptive structures, such as the target surfaces and the solar panel supports; and finally the body-fixed coordinate system can be determined by the geometric characteristics of these structures. The body-fixed coordinate system obtained above can be used to determine the pose of consecutive point clouds of the target, that is, to realize the pose initialization and the pose tracking, and accumulated bias often emerges in the pose tracking. To mitigate the accumulated bias, a pose graph optimization method is adopted. In the end of this article, the performance of the proposed method is evaluated by numerical simulations. Simulation results show that when the distance between the target and the chaser is 10 m, the errors of the estimation results of the target’s attitude and position are 0.025° and 0.026 m, respectively. This means that the proposed method can achieve high-precision pose estimation of the noncooperative target.

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