Real-Time Inverse Kinematics for Humans

Abstract

A general methodology and associated computational algorithm for predicting realistic postures of digital humans (mannequins) is presented. The basic plot for this effort is a task-based approach, where we believe that humans assume different postures for different tasks. The underlying problem is characterized by the calculation (or prediction) of the joint displacements of the human body in such a way 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. The problem is formulated as a multi-objective optimization algorithm where one or more cost functions are considered as objective functions that drive the model to a solution. The formulation is then validated against existing posture prediction algorithms and confirmed with human experimental data.

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Document Details

Document Type
Technical Report
Publication Date
Mar 15, 2004
Accession Number
ADA583113

Entities

People

  • J. Mun
  • Jeremy Yang
  • K. Abdel-malek
  • K. Nebel
  • Z. Mi

Organizations

  • University of Iowa

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer-Aided Design
  • Displacement
  • Engineering
  • Genetic Algorithms
  • Human Body
  • Kinematics
  • Mechanical Engineering
  • Motor Skills
  • Multiobjective Optimization
  • Numbers
  • Optimization
  • Potential Energy
  • Real Numbers
  • Simulations
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Operations Research
  • Robotics and Automation.