Motor differences identify children with autism engaged in iPad gameplay

Ania Anzulewicz, Krzysiek Sobota, Maria Ferrara, Jonathon T. Delafield-Butt

Research output: Contribution to conferencePoster

Abstract

Autism is a developmental disorder evident from infancy. Yet, its clinical identification is often not possible until after the third year of life. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new marker for its early identification. We employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3-6 years old with autism and 45 age- and gender-matched children developing typically. Machine learning analysis of the children’s motor patterns identified autism with 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be assessed by smart device gameplay.

Conference

ConferenceInternational Meeting for Autism Research
CountryUnited States
CityBaltimore
Period11/05/1614/05/16
Internet address

Fingerprint

Autistic Disorder
autism
Gestures
Biomechanical Phenomena
Handheld Computers
developmental disorder
Touch
contact
Equipment and Supplies
gender
learning
evidence
Identification (Psychology)

Keywords

  • autism spectrum disorder
  • mobile devices
  • motor differences
  • intentional movements
  • prospective planning
  • execution of movements
  • movement patterns
  • finger swipe kinematics

Cite this

Anzulewicz, A., Sobota, K., Ferrara, M., & Delafield-Butt, J. T. (2016). Motor differences identify children with autism engaged in iPad gameplay. Poster session presented at International Meeting for Autism Research, Baltimore, United States.
Anzulewicz, Ania ; Sobota, Krzysiek ; Ferrara, Maria ; Delafield-Butt, Jonathon T. / Motor differences identify children with autism engaged in iPad gameplay. Poster session presented at International Meeting for Autism Research, Baltimore, United States.
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Anzulewicz, A, Sobota, K, Ferrara, M & Delafield-Butt, JT 2016, 'Motor differences identify children with autism engaged in iPad gameplay' International Meeting for Autism Research, Baltimore, United States, 11/05/16 - 14/05/16, .

Motor differences identify children with autism engaged in iPad gameplay. / Anzulewicz, Ania; Sobota, Krzysiek; Ferrara, Maria; Delafield-Butt, Jonathon T.

2016. Poster session presented at International Meeting for Autism Research, Baltimore, United States.

Research output: Contribution to conferencePoster

TY - CONF

T1 - Motor differences identify children with autism engaged in iPad gameplay

AU - Anzulewicz, Ania

AU - Sobota, Krzysiek

AU - Ferrara, Maria

AU - Delafield-Butt, Jonathon T.

PY - 2016/5/11

Y1 - 2016/5/11

N2 - Autism is a developmental disorder evident from infancy. Yet, its clinical identification is often not possible until after the third year of life. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new marker for its early identification. We employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3-6 years old with autism and 45 age- and gender-matched children developing typically. Machine learning analysis of the children’s motor patterns identified autism with 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be assessed by smart device gameplay.

AB - Autism is a developmental disorder evident from infancy. Yet, its clinical identification is often not possible until after the third year of life. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new marker for its early identification. We employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3-6 years old with autism and 45 age- and gender-matched children developing typically. Machine learning analysis of the children’s motor patterns identified autism with 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be assessed by smart device gameplay.

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KW - mobile devices

KW - motor differences

KW - intentional movements

KW - prospective planning

KW - execution of movements

KW - movement patterns

KW - finger swipe kinematics

UR - http://www.autism-insar.org/imfar-annual-meeting/imfar

UR - https://imfar.confex.com/imfar/2016/webprogram/Paper23137.html

M3 - Poster

ER -

Anzulewicz A, Sobota K, Ferrara M, Delafield-Butt JT. Motor differences identify children with autism engaged in iPad gameplay. 2016. Poster session presented at International Meeting for Autism Research, Baltimore, United States.