Optimization-based posture prediction for human upper body

Robotica ◽  
2009 ◽  
Vol 27 (4) ◽  
pp. 607-620 ◽  
Author(s):  
Zan Mi ◽  
Jingzhou (James) Yang ◽  
Karim Abdel-Malek
Keyword(s):  
Performance Measures ◽  
Degrees Of Freedom ◽  
Human Performance ◽  
Computational Algorithm ◽  
Performance Measure ◽  
Upper Body ◽  
Cost Functions ◽  
Posture Prediction ◽  
Joint Limits ◽  
Digital Human

SUMMARYA general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures.

Multi-objective optimization-based method for kinematic posture prediction: development and validation

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 245-253 ◽  
Author(s):  
Jingzhou (James) Yang ◽  
Tim Marler ◽  
Salam Rahmatalla
Keyword(s):  
Performance Measures ◽  
Human Performance ◽  
Design Tool ◽  
Upper Body ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Validation Criteria ◽  
Posture Prediction ◽  
Development And Validation ◽  
Digital Human

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

3D HUMAN LIFTING MOTION PREDICTION WITH DIFFERENT PERFORMANCE MEASURES

2012 ◽  
Vol 09 (02) ◽  
pp. 1250012 ◽  
Author(s):  
YUJIANG XIANG ◽  
JASBIR S. ARORA ◽  
KARIM ABDEL-MALEK
Keyword(s):  
Performance Measures ◽  
Shear Force ◽  
Degrees Of Freedom ◽  
Human Performance ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Performance Measure ◽  
Maximum Pressure ◽  
Pressure Force ◽  
Digital Human

This paper presents an optimization-based method for predicting a human dynamic lifting task. The three-dimensional digital human skeletal model has 55 degrees of freedom. Lifting motion is generated by minimizing an objective function (human performance measure) subjected to basic physical and kinematical constraints. Four objective functions are investigated in the formulation: the dynamic effort, the balance criterion, the maximum shear force at spine joint and the maximum pressure force at spine joint. The simulation results show that various human performance measures predict different lifting strategies: the balance and shear force performance measures predict back-lifting motion and the dynamic effort and pressure force performance measures generate squat-lifting motion. In addition, the effects of box locations on the lifting strategies are also studied. All kinematics and kinetic data are successfully predicted for the lifting motion by using the predictive dynamics algorithm and the optimal solution was obtained in about one minute.

Posture Prediction Versus Inverse Kinematics

2001 ◽  
Author(s):  
Karim Abdel-Malek ◽  
Wei Yu ◽  
Zan Mi ◽  
E. Tanbour ◽  
M. Jaber
Keyword(s):  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Human Performance ◽  
Inverse Kinematic ◽  
Cost Functions ◽  
Optimization Approach ◽  
Final Position ◽  
Serial Robot ◽  
Posture Prediction

Abstract Inverse kinematics is concerned with the determination of joint variables of a manipulator given its final position or final position and orientation. Posture prediction also refers to the same problem but is typically associated with models of the human limbs, in particular for postures assumed by the torso and upper extremities. There has been numerous works pertaining to the determination and enumeration of inverse kinematic solutions for serial robot manipulators. Part of these works have also been directly extended to the determination of postures for humans, but have rarely addressed the choice of solutions undertaken by humans, but have focused on purely kinematic solutions. In this paper, we present a theoretical framework that is based on cost functions as human performance measures, subsequently predicting postures based on optimizing one or more of such cost functions. This paper seeks to answer two questions: (1) Is a given point reachable (2) If the point is reachable, we shall predict a realistic posture. We believe that the human brain assumes different postures driven by the task to be executed and not only on geometry. Furthermore, because of our optimization approach to the inverse kinematics problem, models with large number of degrees of freedom are addressed. The method is illustrated using several examples.

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.

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.

The Iowa Interactive Digital-Human Virtual Environment

2004 ◽  
Author(s):  
Jingzhou Yang ◽  
Karim Abdel-Malek ◽  
Kim Farrell ◽  
Kyle Nebel
Keyword(s):  
Virtual Environment ◽  
Degrees Of Freedom ◽  
Human Model ◽  
Cost Functions ◽  
Objective Functions ◽  
Multi Objective ◽  
Cad System ◽  
Time Simulation ◽  
Digital Humans ◽  
Digital Human

Proper assessment of human reach posture is one of the essential functions for workspace design and evaluation in a CAD system with a built-in human model. Most existing models have used heuristic methods, which provide only the range of feasible human reaching postures, which may or may not include naturalistic reach posture. We present a multi-objective-optimization (MOO)-based approach for predicting realistic reach postures of digital humans, one based on our belief that humans assume different reach postures depending on different cost functions, i.e., multi-objective functions. In this work, the number of degrees of freedom (DOF) associated with the model is unlimited, and ranges of motion of joints are considered. The problem is formulated as MOO and single-objective optimization (SOO) algorithms where one or all cost functions (joint displacement, energy, effort, etc.) are considered as objective functions that drive the model to a solution set. A real-time simulation of the digital human’s motion is applied in the IOWA digital-human virtual environment.

Optimization-Based Digital Human Dynamics: Santos™ Walking Backwards

2007 ◽  
Author(s):  
Hyun Jung Kwon ◽  
Yujiang Xiang ◽  
Salam Rahmatalla ◽  
R. Timothy Marler ◽  
Karim Abdel-Malek ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Joint Angle ◽  
Spline Interpolation ◽  
Performance Measure ◽  
Degree Of Freedom ◽  
Human Model ◽  
Entire Body ◽  
Joint Torques ◽  
Digital Human

An objective of this study is to simulate the backward walking motion of a full-body digital human model. The model consists of 55 degree of freedom – 6 degrees of freedom for global translation and rotation and 49 degrees of freedom representing the kinematics of the entire body. The resultant action of all the muscles at a joint is represented by the torque for each degree of freedom. The torques and angles at a joint are treated as unknowns in the optimization problem. The B-spline interpolation is used to represent the time histories of the joint angles and the well-established robotics formulation of the Denavit-Hartenberg method is used for kinematics analysis of the mechanical system. The recursive Lagrangian formulation is used to develop the equations of motion, and was chosen because of its known computational efficiency. The backwards walking problem is formulated as a nonlinear optimization problem. The control points of the B-splines for the joint angle profiles are treated as the design variables. For the performance measure, total dynamic effort that is represented as the integral of the sum of the squares of all the joint torques is minimized using a sequential quadratic programming algorithm. The solution is simulated in the Santos™ environment. Results of the optimization problem are the torque and joint angle profiles. The torques at the key joints and the ground reaction forces are compared to those for the forward walk in order to study the differences between the two walking patterns. Simulation results are approximately validated with the experimental data which is motion captured in the VSR Lab at the University of Iowa.

Effect of Uncertainty on Human Posture Prediction

2014 ◽  
Author(s):  
Ning Li ◽  
James Yang
Keyword(s):  
Center Of Mass ◽  
Interaction Force ◽  
Upper Body ◽  
Random Parameters ◽  
Link Length ◽  
Deterministic Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Posture Prediction ◽  
Digital Human

Posture prediction is a key component in digital human modeling and simulation. Deterministic optimization-based posture prediction formulations have been proposed. However, there exist uncertainties in human anthropometric (human height, link length, center of mass of segments, and moment of inertia, etc.) and environment parameters (location, interaction force), which affects the predicted posture. This paper attempts to study the effect of uncertainty on predicted posture. The single-loop reliability based design optimization (RBDO) method is adapted to predict posture under uncertainties. All random parameters are assumed to have normal distribution. A 24-degree of freedom (DOF) upper body model is used. In this pilot study, it is assumed that one link length and one joint angle are random parameters. The other design variables and parameters are deterministic. With the empirical rule, three cases are investigated for posture prediction. SNOPT software solver is employed to solve the optimization problem. Through comparison with deterministic optimization result and experimental data, the predicted postures from RBDO simulation show that the reliability index affects the predicted posture to some extent.

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