Evaluation of driving posture prediction in digital human simulation using RAMSIS

2011 ◽  
Author(s):  
Jangwoon Park ◽  
Kihyo Jung ◽  
Joonho Chang ◽  
Jeongung Kwon ◽  
Heecheon You
Keyword(s):  
Posture Prediction ◽  
Driving Posture ◽  
Digital Human ◽  
Human Simulation

Santos: A physics-based digital human simulation environment

2006 ◽  
Author(s):  
Karim Abdel-Malek ◽  
Jasbir Arora ◽  
Jingzhou Yang ◽  
Timothy Marler ◽  
Steve Beck ◽  
...  
Keyword(s):  
Simulation Environment ◽  
Digital Human ◽  
Human Simulation

scholarly journals Mapping Center Of Pressure during Standing Reach Tasks

2019 ◽  
Vol 63 (1) ◽  
pp. 1008-1012
Author(s):  
Monica L. H. Jones ◽  
Sheila M. Ebert ◽  
Clive D’Souza ◽  
Matthew P. Reed
Keyword(s):  
Material Handling ◽  
Target Location ◽  
Center Of Pressure ◽  
Digital Human Modeling ◽  
Foot Placement ◽  
Wide Range ◽  
Modeling Software ◽  
Posture Prediction ◽  
Human Figure ◽  
Digital Human

Postural stability and balance during manual material handling and industrial tasks are fundamental to ergonomic assessment of workplace tasks. Previous research has determined that accurate prediction of a person’s balance maintenance strategy is one of the most important parameters affecting the accuracy of posture prediction algorithms. Digital human modeling has the potential to provide designers with accurate tools to represent human posture, but currently available software typically lacks empirically-derived models of center of pressure (CoP) excursion. This paper presents an overview of a study that systematically quantified CoP excursion behavior through a series of standing reach tasks for participants with a wide range of body size. CoP excursion was greatly affected by foot placement and target location. The overall goal of this research is to develop an empirical model of center of pressure (CoP) excursion that can be integrated into human figure modeling software to improve prediction of standing postures typically observed in industrial tasks.

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 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.

Quantitative Analysis of Influence of Cab Package Parameters on Driving Posture

2013 ◽  
Vol 850-851 ◽  
pp. 359-362
Author(s):  
Jian Hui Yao ◽  
Zhan Rui Wang ◽  
Qi Lu
Keyword(s):  
Quantitative Analysis ◽  
Regression Method ◽  
Steering Wheel ◽  
Design And Optimization ◽  
Clutch Pedal ◽  
Regression Functions ◽  
Posture Prediction ◽  
Driving Posture ◽  
The Relationship

In this research, a virtual cab package model had been established in CATIA which was used for driving posture prediction in RAMSIS. 40 cab package schemes and the corresponding driving postures could be acquired by adjusting the parameters of the steering wheel, the clutch pedal and the seat. The quantitative analysis between cab package parameters and driving posture was researched using regression method. And the regression functions that clearly express the relationship between them were concluded, which could be used for directing the design and optimization of the cab package schemes.

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