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

van Diest M, Stegenga J, Wörtche HJ, Roerdink JB, Verkerke GJ, Lamoth CJ - PLoS ONE (2015)

Bottom Line: Results showed that TTvar was significantly higher in older adults than in 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


Average BMU trajectories of young and older adults in ‘neutral’ and ‘maximum sway frequency’ conditions.Average sway trajectories displayed by connecting BMUs of subsequent input posture vectors of all young and older adults (upper and lower panels, respectively) in the neutral game condition and maximum sway frequency condition (left and right panels respectively). Blue circles indicate standard errors of the mean of BMU positions. Stick figures indicate the average adopted postures 1, 2, 3 and 4 captured at 1%, 25%, 50% and 80% of the time of the sway movement, respectively.
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pone.0134350.g004: Average BMU trajectories of young and older adults in ‘neutral’ and ‘maximum sway frequency’ conditions.Average sway trajectories displayed by connecting BMUs of subsequent input posture vectors of all young and older adults (upper and lower panels, respectively) in the neutral game condition and maximum sway frequency condition (left and right panels respectively). Blue circles indicate standard errors of the mean of BMU positions. Stick figures indicate the average adopted postures 1, 2, 3 and 4 captured at 1%, 25%, 50% and 80% of the time of the sway movement, respectively.

Mentions: Comparison of postural control patterns of young and older adults under various task complexity conditions showed significant effects of group on TTvar for the exergame trials where subjects were instructed to sway at maximum sway frequency and maximum sway amplitude (Fig 3). Older adults displayed higher values for TTvar than young adults in these two conditions (7.0 vs 5.5, p = 0.01 and 5.6 vs 4.4, p = 0.02 resp.), indicating that the sway cycles performed by the older adults are more variable than those of young adults, as illustrated in Fig 4. No significant group effects were found for the conditions ‘Neutral’ (p = 0.66), ‘Increased game speed’ (p = 0.31), and ‘Leg lifted’ (p = 0.53). Evaluation of the distribution of variability over the two phases of the trajectory showed a condition effect (p<0.01) on the variability per BMU, but no main effects of age or sway phase. When the variability was split in a vertical and horizontal component (Fig 5), an effect of condition and sway phase on both the vertical and the horizontal component of the variability was observed (p<0.01).


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

van Diest M, Stegenga J, Wörtche HJ, Roerdink JB, Verkerke GJ, Lamoth CJ - PLoS ONE (2015)

Average BMU trajectories of young and older adults in ‘neutral’ and ‘maximum sway frequency’ conditions.Average sway trajectories displayed by connecting BMUs of subsequent input posture vectors of all young and older adults (upper and lower panels, respectively) in the neutral game condition and maximum sway frequency condition (left and right panels respectively). Blue circles indicate standard errors of the mean of BMU positions. Stick figures indicate the average adopted postures 1, 2, 3 and 4 captured at 1%, 25%, 50% and 80% of the time of the sway movement, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4521917&req=5

pone.0134350.g004: Average BMU trajectories of young and older adults in ‘neutral’ and ‘maximum sway frequency’ conditions.Average sway trajectories displayed by connecting BMUs of subsequent input posture vectors of all young and older adults (upper and lower panels, respectively) in the neutral game condition and maximum sway frequency condition (left and right panels respectively). Blue circles indicate standard errors of the mean of BMU positions. Stick figures indicate the average adopted postures 1, 2, 3 and 4 captured at 1%, 25%, 50% and 80% of the time of the sway movement, respectively.
Mentions: Comparison of postural control patterns of young and older adults under various task complexity conditions showed significant effects of group on TTvar for the exergame trials where subjects were instructed to sway at maximum sway frequency and maximum sway amplitude (Fig 3). Older adults displayed higher values for TTvar than young adults in these two conditions (7.0 vs 5.5, p = 0.01 and 5.6 vs 4.4, p = 0.02 resp.), indicating that the sway cycles performed by the older adults are more variable than those of young adults, as illustrated in Fig 4. No significant group effects were found for the conditions ‘Neutral’ (p = 0.66), ‘Increased game speed’ (p = 0.31), and ‘Leg lifted’ (p = 0.53). Evaluation of the distribution of variability over the two phases of the trajectory showed a condition effect (p<0.01) on the variability per BMU, but no main effects of age or sway phase. When the variability was split in a vertical and horizontal component (Fig 5), an effect of condition and sway phase on both the vertical and the horizontal component of the variability was observed (p<0.01).

Bottom Line: Results showed that TTvar was significantly higher in older adults than in 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.