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Optimization-based posture prediction for human upper body

Published online by Cambridge University Press:  01 July 2009

Zan Mi ,
Jingzhou (James) Yang  and
Karim Abdel-Malek
Zan Mi
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242-1000.
Jingzhou (James) Yang*
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242-1000. Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 97409-1021.
Karim Abdel-Malek
Affiliation:
Center for Computer-Aided Design, The University of Iowa, Iowa City, IA 52242-1000.
*
*Corresponding author. E-mail: james.yang@ttu.edu
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Summary

A 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.

Keywords

Posture predictionInverse kinematicsHuman postures

Information

Type
Article
Information
Robotica , Volume 27 , Issue 4 , July 2009 , pp. 607 - 620
Copyright
Copyright © Cambridge University Press 2008

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