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Visual Data Exploration for Balance Quantification in Real-Time During Exergaming.

Venustiano Soancatl Aguilar1, Jasper J van de Gronde1, Claudine J C Lamoth2

  • 1Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands.

Plos One
|January 31, 2017
PubMed
Summary
This summary is machine-generated.

This study explores using exergames to improve balance in older adults. Researchers identified speed, curvature, and turbulence as key measures for real-time balance quantification in exergaming.

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Unintentional injuries, particularly falls, are a leading cause of death in older adults.
  • Traditional balance training programs suffer from low adherence due to lack of engagement and practical barriers.
  • Exergames offer a promising, engaging alternative for balance improvement, but require real-time balance quantification for adaptive difficulty.

Purpose of the Study:

  • To identify effective measures for real-time balance quantification during exergaming.
  • To analyze exergaming data using multidimensional exploratory data analysis and visualization techniques.
  • To assess the potential of these measures for adapting exergame difficulty to individual skill levels.

Main Methods:

  • Collected force plate data from 40 participants (ages 20-79) during exergaming.
  • Utilized visualization techniques like heat maps and violin plots for initial data exploration.
  • Extracted and analyzed features including speed, dispersion, turbulence, and curvature.

Main Results:

  • Heat maps and violin plots effectively provided initial insights into exergaming data.
  • Speed, curvature, and a turbulence measure emerged as promising indicators of real-time balance.
  • These selected measures demonstrated age-related changes in balance performance.

Conclusions:

  • Exergames hold potential for improving balance and reducing fall risk in older adults.
  • Speed, curvature, and turbulence are viable metrics for real-time balance assessment in exergames.
  • Future work will involve applying these techniques to whole-body movement data from motion capture systems.