title: Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.
abstract: BACKGROUND
  Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame.
  
  
  METHODS
  Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features.
  
  
  RESULTS
  Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%.
  
  
  CONCLUSIONS
  Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.
authors.affiliation: INCAS3, Assen, The Netherlands; University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.
authors.forename: Mike
authors.initials: M
authors.lastname: van Diest
authors.affiliation: INCAS3, Assen, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Stegenga
authors.affiliation: INCAS3, Assen, The Netherlands.
authors.forename: Heinrich J
authors.initials: HJ
authors.lastname: Wörtche
authors.affiliation: University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, Groningen, The Netherlands.
authors.forename: Jos B T M
authors.initials: JB
authors.lastname: Roerdink
authors.affiliation: University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Center for Rehabilitation, Groningen, The Netherlands; University of Twente, Department of Biomechanical Engineering, Enschede, The Netherlands.
authors.forename: Gijsbertus J
authors.initials: GJ
authors.lastname: Verkerke
authors.affiliation: University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands.
authors.forename: Claudine J C
authors.initials: CJ
authors.lastname: Lamoth
mesh_terms: D000328:Adult; D015444:Exercise; D006801:Humans; D009068:Movement; D015999:Multivariate Analysis; D011187:Posture; D055815:Young Adult