Real-time inverse kinematics for humans

Zan Mi, Jingzhou Yang, Karim Abdel-Malek, Joung H. Mun, Kyle Nebel

Research output: Contribution to conferencePaperpeer-review

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.

Original languageEnglish
Pages349-359
Number of pages11
DOIs
StatePublished - 2002
Event27th Biennial Mechanisms and Robotics Conference - Montreal, Que., Canada
Duration: Sep 29 2002Oct 2 2002

Conference

Conference27th Biennial Mechanisms and Robotics Conference
Country/TerritoryCanada
CityMontreal, Que.
Period09/29/0210/2/02

Keywords

  • Human postures
  • Inverse kinematics
  • Posture prediction
  • Task-based postures

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