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, 9 (1), 20094

Whole-Body Movement During Videogame Play Distinguishes Youth With Autism From Youth With Typical Development

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Whole-Body Movement During Videogame Play Distinguishes Youth With Autism From Youth With Typical Development

Adel Ardalan et al. Sci Rep.

Abstract

Individuals with autism spectrum disorder struggle with motor difficulties throughout the life span, and these motor difficulties may affect independent living skills and quality of life. Yet, we know little about how whole-body movement may distinguish individuals with autism spectrum disorder from individuals with typical development. In this study, kinematic and postural sway data were collected during multiple sessions of videogame play in 39 youth with autism spectrum disorder and 23 age-matched youth with typical development (ages 7-17 years). The youth on the autism spectrum exhibited more variability and more entropy in their movements. Machine learning analysis of the youths' motor patterns distinguished between the autism spectrum and typically developing groups with high aggregate accuracy (up to 89%), with no single region of the body seeming to drive group differences. Moreover, the machine learning results corresponded to individual differences in performance on standardized motor tasks and measures of autism symptom severity. The machine learning algorithm was also sensitive to age, suggesting that motor challenges in autism may be best characterized as a developmental motor delay rather than an autism-distinct motor profile. Overall, these results reveal that whole-body movement is a distinguishing feature in autism spectrum disorder and that movement atypicalities in autism are present across the body.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Study setup. Microsoft Kinect camera and Wii Balance Board send the joint kinematic and postural sway data to the computer, which controls the biofeedback-based balance training game and records the data.
Figure 2
Figure 2
Correlation analysis of the distance from classification boundary vs. (A) age, (B) overall motor performance and (C) social responsiveness symptoms.
Figure 3
Figure 3
Top-ranked body features used by RF classifiers, marked as red stars.
Figure 4
Figure 4
Distributions of top-ranked feature values for ASD vs. TD participants, and Cohen’s d effect sizes of their differences.
Figure 5
Figure 5
Architecture of our data acquisition and analysis pipeline.

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