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Experimental Methods to Study Human Postural Control
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Predicting reactive stepping in response to perturbations by using a classification approach.

Amber R Emmens1, Edwin H F van Asseldonk2, Vera Prinsen2

  • 1Department of Biomechanical Engineering, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, the Netherlands. a.r.emmens@utwente.nl.

Journal of Neuroengineering and Rehabilitation
|July 4, 2020
PubMed
Summary

This study accurately predicts reactive stepping in humans using a data-driven approach. Key features like center of mass acceleration and velocity were identified as crucial for balance control predictions.

Keywords:
ClassificationPostural controlReactive steppingStanding balance

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Area of Science:

  • Biomechanics
  • Human motor control
  • Robotics

Background:

  • Human balance control involves strategies like reactive stepping.
  • Understanding strategy transitions is key to human balance research.
  • Previous models of reactive stepping were limited in scope.

Purpose of the Study:

  • Predict reactive stepping in response to a push.
  • Identify key features influencing the decision to step.
  • Utilize a data-driven approach for prediction.

Main Methods:

  • Ten subjects received forward pushes, responding naturally.
  • Kinematic and center of pressure data were collected.
  • Classification algorithms trained to predict reactive stepping.

Main Results:

  • Neural networks achieved higher prediction accuracy than other algorithms.
  • Prediction accuracy increased with a longer observation time window.
  • Key features identified: center of mass acceleration/velocity, cervical joint position.

Conclusions:

  • A data-driven method accurately predicts reactive stepping.
  • The identified feature set differs from previous studies.
  • Potential applications include robotic balance controllers for exoskeletons.