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A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance
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Predicting multiple step placements for human balance recovery tasks.

Zohaib Aftab1, Thomas Robert, Pierre-Brice Wieber

  • 1Université de Lyon, F-69622, Lyon, France IFSTTAR, LBMC, F-69675 Bron Université Lyon 1, LBMC, F-69373 Lyon, France.

Journal of Biomechanics
|September 25, 2012
PubMed
Summary

This study presents a new model for predicting multiple balance recovery steps after a perturbation. The model accurately predicts foot placements, improving upon previous methods by considering step execution time and multiple steps.

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Area of Science:

  • Biomechanics
  • Robotics
  • Human Balance Control

Background:

  • Stepping is a primary human strategy for recovering balance from external perturbations.
  • Existing balance recovery models have limitations, including neglecting step execution time and only considering single-step responses.

Purpose of the Study:

  • To develop a simplified balance recovery model capable of predicting complete, multiple-step recovery responses.
  • To overcome limitations of previous models by incorporating dynamic aspects of balance restoration.

Main Methods:

  • Adapted a control scheme from biped robot locomotion, utilizing a Linear Model Predictive Controller (LMPC).
  • The LMPC estimates optimal foot placements to decelerate the Center of Mass (CoM) and achieve a steady posture.
  • Model predictions were validated against experimental data from tether-release perturbation studies.

Main Results:

  • The developed model accurately predicted step placements for various perturbation intensities.
  • The model successfully reproduced both single-step and multiple-step recovery responses observed in experiments.
  • Predictions showed good agreement with previously reported experimental data under tether-release conditions.

Conclusions:

  • The proposed LMPC-based model effectively predicts human balance recovery strategies, including multiple steps.
  • This approach offers a significant advancement over prior models by addressing key limitations.
  • The model demonstrates potential for simulating diverse balance recovery scenarios, despite current limitations (e.g., sagittal plane focus).