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.

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.

Do Arm Postures Vary With the Speed of Reaching?

1999 ◽  
Vol 81 (5) ◽  
pp. 2582-2586 ◽  
Author(s):  
Kiisa C. Nishikawa ◽  
Sara T. Murray ◽  
Martha Flanders
Keyword(s):  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Reaching Movements ◽  
Total Force ◽  
Joint Torques ◽  
Dynamic Factors ◽  
Human Arm ◽  
Wide Range ◽  
Dynamic Forces

Do arm postures vary with the speed of reaching? For reaching movements in one plane, the hand has been observed to follow a similar path regardless of speed. Recent work on the control of more complex reaching movements raises the question of whether a similar “speed invariance” also holds for the additional degrees of freedom. Therefore we examined human arm movements involving initial and final hand locations distributed throughout the three-dimensional (3D) workspace of the arm. Despite this added complexity, arm kinematics (summarized by the spatial orientation of the “plane of the arm” and the 3D curvature of the hand path) changed very little for movements performed over a wide range of speeds. If the total force (dynamic + quasistatic) had been optimized by the control system (e.g., as in a minimization of the change in joint torques or the change in muscular forces), the optimal solution would change with speed; slow movements would reflect the minimal antigravity torques, whereas fast movements would be more strongly influenced by dynamic factors. The speed-invariant postures observed in this study are instead consistent with a hypothesized optimization of only the dynamic forces.

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.

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 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.

Optimal Posture and Supporting Hand Force Prediction for Common Automotive Assembly One-Handed Tasks

2014 ◽  
Vol 6 (2) ◽  
Author(s):  
Bradley Howard ◽  
James Yang ◽  
Burak Ozsoy
Keyword(s):  
Stability Analysis ◽  
Degrees Of Freedom ◽  
Human Performance ◽  
Performance Measure ◽  
Joint Torque ◽  
External Support ◽  
Human Model ◽  
Automotive Assembly ◽  
External Forces ◽  
Hand Forces

People often complete tasks using one hand for the task and one hand for support. These one-handed support tasks can be found in many different types of jobs, such as automotive assembly jobs. Optimization-based posture prediction has proven to be a valid tool in predicting the postures necessary to complete the tasks, but the related external support forces have been prescribed and not predicted. This paper presents a method in which the optimal posture and related supporting hand forces can be predicted simultaneously using optimization and stability analysis techniques. Postures are evaluated using a physics-based human performance measure (HPM) while external forces are assessed using stability analysis. The physics-based performance measures are based on joint torque. Stability is analyzed using criteria based on a 3D zero moment point (ZMP). The human model used in the prediction contains 56 degrees of freedom and is based on a 50th percentile female in stature. Tasks based on common automotive assembly one-handed tasks found in literature are considered as examples to test the proposed method. Overall, the predicted supporting hand forces have good correlation with experimentally measured forces.

Models, Measures, and Judgments in System Design

1969 ◽  
Vol 11 (6) ◽  
pp. 577-590 ◽  
Author(s):  
William B. Knowles ◽  
William J. Burger ◽  
Meredith B. Mitchell ◽  
Donald T. Hanifan ◽  
Joseph W. Wulfeck
Keyword(s):  
System Design ◽  
Performance Measures ◽  
Analytical Modeling ◽  
Human Performance ◽  
Practical Method ◽  
Performance Measure ◽  
Performance Data ◽  
Analytical Models ◽  
Design Models

This paper assumes increasing use of analytical models in system design. Some characteristics of such models and requirements for human performance data compatible with them are discussed. Methods of obtaining human performance data for use in design models are considered. The use of expert judges to generate performance measures is reviewed. Two new studies are reported in support of the proposition that expert judgments may offer a practical method of obtaining performance measure with potentially wide application in analytical modeling efforts.

Effects of Accounting-Method Choices on Subjective Performance-Measure Weighting Decisions: Experimental Evidence on Precision and Error Covariance

2005 ◽  
Vol 80 (4) ◽  
pp. 1163-1192 ◽  
Author(s):  
Ranjani Krishnan ◽  
Joan L. Luft ◽  
Michael D. Shields
Keyword(s):  
Performance Measures ◽  
Error Variance ◽  
Spillover Effects ◽  
Performance Measure ◽  
Incentive System ◽  
Experimental Setting ◽  
Error Covariance ◽  
Psychology Theory ◽  
Optimal Weighting ◽  
Similarity And Difference

Performance-measure weights for incentive compensation are often determined subjectively. Determining these weights is a cognitively difficult task, and archival research shows that observed performance-measure weights are only partially consistent with the predictions of agency theory. Ittner et al. (2003) have concluded that psychology theory can help to explain such inconsistencies. In an experimental setting based on Feltham and Xie (1994), we use psychology theories of reasoning to predict distinctive patterns of similarity and difference between optimal and actual subjective performance-measure weights. The following predictions are supported. First, in contrast to a number of prior studies, most individuals' decisions are significantly influenced by the performance measures' error variance (precision) and error covariance. Second, directional errors in the use of these measurement attributes are relatively frequent, resulting in a mean underreaction to an accounting change that alters performance measurement error. Third, individuals seem insufficiently aware that a change in the accounting for one measure has spillover effects on the optimal weighting of the other measure in a two-measure incentive system. In consequence, they make performance-measure weighting decisions that are likely to result in misallocations of agent effort.

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