0
Research Papers

Physics-Based Seated Posture Prediction for Pregnant Women and Validation Considering Ground and Seat Pan Contacts

[+] Author and Article Information
Bradley Howard, Aimee Cloutier

Human-Centric Design Research Laboratory, Department of Mechanical Engineering,  Texas Tech University, Lubbock, TX, 79409

Jingzhou (James) Yang1

Human-Centric Design Research Laboratory, Department of Mechanical Engineering,  Texas Tech University, Lubbock, TX, 79409james.yang@ttu.edu

1

Corresponding author.

J Biomech Eng 134(7), 071004 (Jul 13, 2012) (11 pages) doi:10.1115/1.4007006 History: Received September 01, 2011; Revised May 18, 2012; Posted July 06, 2012; Published July 13, 2012; Online July 13, 2012

An understanding of human seated posture is important across many fields of scientific research. Certain demographics, such as pregnant women, have special postural limitations that need to be considered. Physics-based posture prediction is a tool in which seated postures can be quickly and thoroughly analyzed, as long the predicted postures are realistic. This paper proposes and validates an optimization formulation to predict seated posture for pregnant women considering ground and seat pan contacts. For the optimization formulation, the design variables are joint angles (posture); the cost function is dependent on joint torques. Constraints include joint limits, joint torque limits, the distances from the end-effectors to target points, and self-collision avoidance constraints. Three different joint torque cost functions have been investigated to account for the special postural characteristics of pregnant women and consider the support reaction forces (SRFs) associated with seated posture. Postures are predicted for three different reaching tasks in common reaching directions using each of the objective function formulations. The predicted postures are validated against experimental postures obtained using motion capture. A linear regression analysis was used to evaluate the validity of the predicted postures and was the criteria for comparison between the different objective functions. A 56 degree of freedom model was used for the posture prediction. Use of the objective function minimizing the maximum normalized joint torque provided an R2 value of 0.828, proving superior to either of two alternative functions.

FIGURES IN THIS ARTICLE
<>
Copyright © 2012 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

General kinematic model

Grahic Jump Location
Figure 2

56 DOF human model

Grahic Jump Location
Figure 3

General seated posture and the associated forces

Grahic Jump Location
Figure 4

Resultant reaction loads

Grahic Jump Location
Figure 5

Distribution of the forces at the ZMP to the SRF application points

Grahic Jump Location
Figure 6

Constraints associated with seated posture: (a) end-effector distance constraints and (b) self-collision avoidance constraints

Grahic Jump Location
Figure 7

Marker set used for data collection: (a) Front view; (b) back view

Grahic Jump Location
Figure 8

Linear regression of simulation and experiment postures with the objective function: (a) min-max, (b) sum squared normalized joint torque, (c) sum squared joint torque

Grahic Jump Location
Figure 9

Sample predicted postures versus the experimental postures

Grahic Jump Location
Figure 10

Average torso flexion in hip joints for all subjects per task using the min-max formulation including error bars representing one standard deviation

Grahic Jump Location
Figure 11

Average joint angles describing shoulder elevation for the min-max formulation including error bars representing one standard deviation

Grahic Jump Location
Figure 12

Average torque in each spinal joint as a percentage of its respective torque limit

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In