Hybrid method for driver accommodation using optimization-based digital human models

Gragg, Jared; Yang, James; Howard, Brad

doi:10.1016/j.cad.2010.11.009

Cited by 17 publications

(8 citation statements)

References 24 publications

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“…1,32 The third criterion assumes that subjects choose the posture with the greatest stability. 17 Gragg et al 14 determined the driver seat adjustment range by employing this optimization-based posture prediction formulation. Dysart and Woldstad 10 compared these three models.…”

Section: Literature Reviewmentioning

confidence: 99%

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An inverse optimization approach for determining weights of joint displacement objective function for upper body kinematic posture prediction

et al. 2011

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Human posture prediction can often be formulated as a nonlinear multiobjective optimization (MOO) problem. The joint displacement function is considered as a benchmark of human performance measures. When the joint displacement function is used as the objective function, posture prediction is a MOO problem. The weighted-sum method is commonly used to find a Pareto solution of this MOO problem. Within the joint displacement function, the relative value of the weights associated with each joint represents the relative importance of that joint. Usually, weights are determined by trial and error approaches. This paper presents a systematic approach via an inverse optimization approach to determine the weights for the joint displacement function in posture prediction. This inverse optimization problem can be formulated as a bi-level optimization problem. The design variables are joint angles and weights. The cost function is the summation of the differences between two set of joint angles (the design variables and the realistic posture). Constraints include (1) normalized weights within limits and (2) an inner optimization problem to solve for joint angles (predicted posture). Additional constraints such as weight limits and weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 24 degree of freedom human upper body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected to run the experiment. The set of weights for the general seated posture prediction is obtained by averaging all weights for all subjects and all tasks. On the basis of obtained set of weights, the predicted postures match the experimental results well.

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Section: Literature Reviewmentioning

confidence: 99%

“…29,30,[46][47][48]50 Recently, a new pregnant woman model was developed and the MOO posture prediction was implemented. 17 Gragg et al 14 determined the driver seat adjustment range by employing this optimization-based posture prediction formulation.…”

Section: Literature Reviewmentioning

confidence: 99%

An inverse optimization approach for determining weights of joint displacement objective function for upper body kinematic posture prediction

et al. 2011

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“…For the first hypothesis, the seat track location and adjustment ranges based on kinematic posture prediction (Gragg et al, 2012) are compared with those obtained with the physics-based method. For the second hypothesis, the pedal reaction moment is altered from 0 N m to 40 N m in 20 N m increments.…”

Section: Introductionmentioning

confidence: 99%

“…In DHM, for the seat adjustment range prediction problem, mostly boundary manikins ranging from 5th percentile female to 95th percentile male are used. Gragg et al (2012) proposed a hybrid method for predicting the optimum driver seat adjustment range based on an optimization-based kinematic posture prediction method. This hybrid method included a boundary manikin approach, a population sampling approach, and a special population of pregnant women.…”

Section: Introductionmentioning

confidence: 99%

Simulated effect of driver and vehicle interaction on vehicle interior layout

Ozsoy

Yang

et al. 2015

International Journal of Industrial Ergonomics

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“…21À23 A speci¯c example is the use optimization based posture prediction to determine the driver's seat adjustment ranges for di®erent demographics in a typical vehicle. 24,25 In many cases, these computation models have been based on kinematics. With the development of e±cient algorithms for calculating joint torque, 22,26,27 such as the Lagrangian recursive dynamic algorithm, 28 the ability to analyze the dynamics of posture has been realized.…”

Section: Introductionmentioning

confidence: 99%

A New Stability Criterion for Human Seated Tasks With Given Postures

Howard

Yang

2012

Int. J. Human. Robot.

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In digital human modeling (DHM), the analysis of postural stability has¯ve main goals: to determine if a posture is stable or unstable through an explicit criterion; to quantify the level of stability or provide a margin of stability that accounts for the height of the center of mass (COM) above the support plane(s); to be valid in the presence of externally applied forces and moments; be able to assess stability when multiple noncoplanar support planes exist, as is the case with seated postures; and to give insight into the support reaction force (SRF) distribution. To date, there is not a method for analyzing stability that can e®ectively meet each goal. This paper presents a new stability criterion and stability analysis that accomplishes each intended goal. The stability analysis is derived from the calculation of joint torque using the recursive Lagrangian dynamic formulation. A 56-degree-of-freedom (DOF) articulated digital human model is used to model seated postures to demonstrate the proposed stability criterion. Di®erent given postures with di®erent external load cases are presented.

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Hybrid method for driver accommodation using optimization-based digital human models

Cited by 17 publications

References 24 publications

An inverse optimization approach for determining weights of joint displacement objective function for upper body kinematic posture prediction

An inverse optimization approach for determining weights of joint displacement objective function for upper body kinematic posture prediction

Simulated effect of driver and vehicle interaction on vehicle interior layout

A New Stability Criterion for Human Seated Tasks With Given Postures

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