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Updated: Sep 20, 2025

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Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology.

Biwei Tang1, Yaling Peng1, Jing Luo1

  • 1Intelligent System Research Institute, School of Automation, Wuhan University of Technology, Wuhan, China.

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|June 7, 2022
PubMed
Summary
This summary is machine-generated.

This study identifies the hidden cost function in human lifting motions using bilevel optimization. This approach aids in developing better wearable robots to reduce back pain and fatigue.

Keywords:
bilevel optimizationdirect collocationhuman lifting motioninverse optimization controlparticle swarm optimization

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Area of Science:

  • Biomechanics
  • Robotics
  • Optimization

Background:

  • Understanding human lifting mechanics is crucial for designing assistive wearable robots.
  • The central nervous system's complex cost function for human motion is difficult to determine.
  • Inverse optimization control (IOC) offers a method to identify underlying cost functions.

Purpose of the Study:

  • To investigate and identify the underlying cost function of human lifting tasks.
  • To apply bilevel optimization technology for analyzing human motion control.
  • To enhance the design and control of wearable robotic devices for reducing low-back pain and fatigue.

Main Methods:

  • Developed a nested bilevel optimization approach integrating particle swarm optimization (PSO) and the direction collocation (DC) method.
  • Upper level: PSO optimized weighting parameters in the cost function, minimizing kinematic error.
  • Lower level: DC method predicted human kinematics and dynamics using an OpenSim musculoskeletal model.

Main Results:

  • The proposed bilevel optimization method effectively identified the cost function for human lifting tasks.
  • Experimental evaluations across different subjects confirmed the method's efficacy.
  • The approach demonstrated a high degree of accuracy in predicting human motion.

Conclusions:

  • The developed method is a powerful tool for uncovering the cost functions governing human movement.
  • This research provides a significant advancement in the predictive simulation of human lifting motion.
  • The findings support the development of more effective and intuitive wearable robotic systems.