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Related Experiment Video

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Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps

Mike van Diest1, Jan Stegenga2, Heinrich J Wörtche2

  • 1INCAS3, Assen, The Netherlands; University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.

Plos One
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PubMed
Summary
This summary is machine-generated.

Older adults exhibit greater balance sway variability during complex exergaming tasks. Movement pattern analysis can distinguish age groups, aiding in developing balance training algorithms.

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Area of Science:

  • Gerontology
  • Biomechanics
  • Human-Computer Interaction

Background:

  • Exergames are increasingly used for balance training in older adults to prevent falls.
  • Real-time balance assessment in exergames can provide personalized feedback and adaptive gameplay.
  • Algorithms for quantifying balance control during exergaming are needed.

Purpose of the Study:

  • To identify movement patterns and their variability in young and older adults during a weight-shifting exergame.
  • To develop and evaluate algorithms for automatic balance control assessment.

Main Methods:

  • Twenty young and twenty older adults played a weight-shifting exergame under varying complexity.
  • Multi-segmental whole-body movement data were captured using Kinect.
  • Self Organizing Maps (SOM) identified movement patterns, and Total Trajectory Variability (TTvar) quantified pattern variability. A k Nearest Neighbor (kNN) classifier was used for age discrimination.

Main Results:

  • Older adults showed significantly higher TTvar than young adults under complex task conditions.
  • The kNN classifier achieved 65.8% accuracy in discriminating between young and older adults based on SOM features.

Conclusions:

  • Older adults demonstrate more variable sway behavior during complex exergaming.
  • Exergame movement patterns can differentiate age groups, though with limited accuracy.
  • Findings support the development of algorithms for home-based balance training exergames.