Human Carrying Simulation With Symmetric and Asymmetric Loads

2012 ◽  
Author(s):  
Yujiang Xiang ◽  
Jasbir S. Arora ◽  
Salam Rahmatalla ◽  
Hyun-Joon Chung ◽  
Rajan Bhatt ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Human Motion ◽  
Human Model ◽  
Physical Constraints ◽  
Digital Human Model ◽  
Load Carrying ◽  
Reaction Forces ◽  
Asymmetric Load ◽  
Optimization Schemes ◽  
Digital Human

Human carrying is simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). Predictive dynamics approach is used to predict the carrying motion with symmetric and asymmetric loads. In this process, the model predicts joints dynamics using optimization schemes and task-based physical constraints. The results indicated that the model can realistically match human motion and ground reaction forces data during symmetric and asymmetric load carrying task. With such prediction capability the model could be used for biomedical and ergonomic studies.

Human Carrying Simulation With Symmetric and Asymmetric Loads Using Optimization

2014 ◽  
Vol 30 (1) ◽  
pp. 140-146 ◽  
Author(s):  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Human Model ◽  
Physical Constraints ◽  
Digital Human Model ◽  
Load Carrying ◽  
Joint Dynamics ◽  
Asymmetric Load ◽  
Optimization Schemes ◽  
Robust Prediction ◽  
Digital Human

Human carrying is simulated in this work by using a skeletal digital human model with 55 degrees of freedom. An optimization-based approach is used to predict the carrying motion with symmetric and asymmetric loads. In this process, the model predicts joint dynamics using optimization schemes and task-based physical constraints. The results indicate that the model can predict different carrying strategies during symmetric and asymmetric load-carrying tasks. The model can also indicate the risk factors for extreme loading situations. With such robust prediction capability, the model could be used for biomedical and ergonomic studies.

Human Stair Ascent and Descent Simulations

2014 ◽  
Author(s):  
Yujiang Xiang ◽  
Joo H. Kim ◽  
Hyun-Joon Chung ◽  
James Yang ◽  
Hyun-Jung Kwon
Keyword(s):  
Degrees Of Freedom ◽  
Human Motion ◽  
Stair Climbing ◽  
Human Model ◽  
Physical Constraints ◽  
Digital Human Model ◽  
Stair Ascent ◽  
Reaction Forces ◽  
Optimization Schemes ◽  
Digital Human

Human stair ascent and descent are simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). Hybrid predictive dynamics approach is used to predict the stair climbing motion with weapons and backpacks. In this process, the model predicts joints dynamics using optimization schemes and task-based physical constraints. The results indicated that the model can realistically match human motion and ground reaction forces data during stair climbing tasks. This can be used in human health domain such as leg prosthesis design.

Optimization-Based Prediction of Aiming and Kneeling Military Tasks Performed by a Soldier

2012 ◽  
Author(s):  
Mahdiar Hariri ◽  
Jasbir Arora ◽  
Karim Abdel-Malek
Keyword(s):  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Human Model ◽  
Ground Reaction Forces ◽  
Optimization Approach ◽  
Ground Contact ◽  
Digital Human Model ◽  
Reaction Forces ◽  
Digital Human ◽  
The Given

The objective of this study is to predict the “Aiming While Standing” and “Aiming While Kneeling” motion tasks for a soldier (human) using a full-body, three dimensional digital human model. The digital human is modeled as a 55 degree of freedom branched mechanism. Six degrees of freedom specify the global position and orientation of the coordinate frame attached to the pelvis of the digital human and 49 degrees of freedom represent the revolute joints which model the human joints and determine the kinematics of the entire digital human. Motion is generated by a multi-objective optimization approach minimizing the mechanical energy and joint discomfort simultaneously. A sequential quadratic programming (SQP) algorithm in SNOPT is used to solve the nonlinear optimization problem. The optimization problem is subject to constraints which represent the limitations of the environment, the digital human model and the motion task. Design variables are the joint angle profiles. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces. The feasibility of the generation of that arbitrary motion by using the given ground contact areas is ensured by using the well known Zero Moment Point (ZMP) constraint. During the kneeling motion, different parts of the body come in contact and lose contact with the ground which is modeled using a general approach. The ground reaction force on each transient ground contact area is determined using the equations of motion. It is assumed that enough friction exists that allow the human to generate reaction forces as determined by the ZMP constraint. Using these ground reaction forces, the required torques at all joints are calculated by the recursive Lagrangian formulation. Using the given method, we can predict realistic motions for the “Aiming While Standing” and “Aiming While Kneeling” tasks. The optimization approach is able to very well predict the “Natural Point of Aim” which is a well known concept for soldiers. In other words, the approach is able to predict the most comfortable final orientation of the feet on the ground for engaging a specific target. We also predict cases where the orientation of the soldier’s feet are enforced. Many virtual experiments have been conducted by changing the target location in the 3D space, changing the anthropometry of the soldier, adding armor to different joints, changing the variable parameters of the rifle, adding backpack and using different weapons.

The Hybrid Predictive Dynamics Method for Analysis, Simulation and Prediction of Human Motion

2016 ◽  
Author(s):  
Mahdiar Hariri
Keyword(s):  
Motion Capture ◽  
Degrees Of Freedom ◽  
Human Motion ◽  
Human Model ◽  
Coordinate Frame ◽  
Optimization Approach ◽  
Predictive Dynamics ◽  
Digital Human Model ◽  
Digital Human ◽  
Dynamics Method

The ‘Hybrid Predictive Dynamics Method for Digital Human Modeling’ is analyzed in this work. The ‘Hybrid’ prefix mentioned in the literature recently [1], refers to the use of motion capture data for improving human motion simulations. This use of motion capture compensates for the inherent weaknesses of purely theoretical motion prediction due to deficiencies in computational power or available theoretical backgrounds. In this work, it is shown that while using the ‘Hybrid’ the more precisely and finely the human motion is modeled (if computational and theoretical limitations allow), the less will be the need for the ‘Hybrid’ method and the more will the human model be able to change the prediction if the inputs are varied (cause and effect). Several human motion scenarios are mentioned in this work. These motion tasks are: “Jogging around Markers”, “Rolling Over”, “Getting up from Prone”, “Vertical Jumping” and “Kneeling and Aiming”. The digital human model is a full-body, three dimensional model with 55 degrees of freedom. Six degrees of freedom specify the global position and orientation of the coordinate frame attached to the pelvic point of the digital human and 49 degrees of freedom represent the revolute joints which model the human joints and determine the kinematics of the entire digital human. Motion is generated by a multi-objective optimization approach. The optimization problem is subject to constraints which represent the limitations of the environment, the digital human model and the motion task. Design variables are the joint angle profiles. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces. The feasibility of the generation of that arbitrary motion by using the given ground contact areas is ensured by using the well-known Zero Moment Point (ZMP) constraint.

Muscle Force Prediction of 2D Gait Using Predictive Dynamics Optimization

2016 ◽  
Author(s):  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Muscle Force ◽  
Optimal Solution ◽  
Human Motion ◽  
Human Gait ◽  
Predictive Dynamics ◽  
Physical Constraints ◽  
Reaction Forces ◽  
Muscle Groups ◽  
Optimization Schemes

Cyclic human gait is simulated in this work by using a 2D musculoskeletal model with 12 degrees of freedom (DOF). Eight muscle groups are modeled on each leg. Predictive dynamics approach is used to predict the walking motion. In this process, the model predicts joints dynamics and muscle forces simultaneously using optimization schemes and task-based physical constraints. The results indicated that the model can realistically match human motion, ground reaction forces (GRF), and muscle force data during walking task. The proposed optimization algorithm is robust and the optimal solution is obtained in seconds. This can be used in human health domain such as leg prosthesis design.

WORKSPACE OF DIGITAL HUMAN LOWER EXTREMITIES

2009 ◽  
Vol 06 (02) ◽  
pp. 291-306 ◽  
Author(s):  
JINGZHOU (JAMES) YANG
Keyword(s):  
Degrees Of Freedom ◽  
Virtual Prototyping ◽  
Lower Extremities ◽  
Complete Information ◽  
Upper Body ◽  
Human Model ◽  
Singular Surfaces ◽  
Rank Deficiency ◽  
Digital Human Model ◽  
Digital Human

This paper presents an applicable formula for determining the workspace of digital human lower extremities. The digital human model has over 100 degrees of freedom (DOF): 94 in the upper body, 14 in the lower extremities, 5 in the neck, 4 in the eyes, and 25 for each hand. The Jacobian row rank deficiency criteria are implemented to determine the singular surfaces that finally form the workspace. The use of this digital human model for determining workspace offers several advantages over direct measurement: (1) the workspace can be visualized in real-time based on offline computation, (2) the workspace can be used for the ergonomic design of products in the virtual prototyping stage, and (3) the calculated workspace includes complete information about the envelope and inside characteristics.

Determining the Static Joint Torques of a Digital Human Model Considering Balance

2009 ◽  
Author(s):  
Jingzhou James Yang ◽  
Yujiang Xiang ◽  
Joo Kim
Keyword(s):  
Human Model ◽  
Joint Torques ◽  
Digital Human Model ◽  
Human Models ◽  
Support Reaction ◽  
Reaction Forces ◽  
Zero Moment ◽  
The Stability ◽  
Physics Based Simulation ◽  
Digital Human

This paper presents a methodology for determining the static joint torques of a digital human model considering balance for both standing and seating tasks. An alternative and efficient formulation of the Zero-Moment Point (ZMP) for static balance and the approximated (ground/seat) support reaction forces/moments are derived from the resultant reaction loads, which includes the gravity and externally applied loads. The proposed method can be used for both standing and seating tasks for assessing the stability/balance of the posture. The proposed formulation can be beneficial to physics-based simulation of humanoids and human models. Also, the calculated joint torques can be considered as an indicator to assess the risks of injuries when human models perform various tasks.

Degrees of freedom coupling adapted to the upper limb of a digital human model

2017 ◽  
Vol 5 (4) ◽  
pp. 314
Author(s):  
Pierre Olivier Lemieux ◽  
Arnaud Barré ◽  
Nicola Hagemeister ◽  
Rachid Aissaoui
Keyword(s):  
Upper Limb ◽  
Degrees Of Freedom ◽  
Human Model ◽  
Digital Human Model ◽  
Digital Human

Effect of Foot Location on Human Running Prediction

2014 ◽  
Author(s):  
Hyun-Joon Chung ◽  
Rajan Bhatt ◽  
Yujiang Xiang ◽  
Jasbir S. Arora ◽  
Karim Abdel-Malek
Keyword(s):  
Degrees Of Freedom ◽  
Reaction Force ◽  
Performance Measure ◽  
Joint Torque ◽  
Upper Body ◽  
Human Model ◽  
Digital Human Model ◽  
Digital Human ◽  
Dynamics Method ◽  
Human Running

Human running is simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effect of foot location on the running motion prediction are simulated and studied.

Degrees of freedom coupling adapted to the upper limb of a digital human model

2017 ◽  
Vol 5 (4) ◽  
pp. 314
Author(s):  
Pierre Olivier Lemieux ◽  
Rachid Aissaoui ◽  
Arnaud Barré ◽  
Nicola Hagemeister
Keyword(s):  
Upper Limb ◽  
Degrees Of Freedom ◽  
Human Model ◽  
Digital Human Model ◽  
Digital Human
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