title: Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism.
abstract: Autism is a developmental disorder evident from infancy. Yet, its clinical identification requires expert diagnostic training. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new computational marker for its early identification. In this study, 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 up to 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 computationally assessed by fun, smart device gameplay.
authors.affiliation: Jan Matejko Academy of Fine Arts, Kraków, Poland; Jagiellonian University, Krakow, Poland.|Harimata Sp. z.o.o., Kraków, Poland.
authors.forename: Anna
authors.initials: A
authors.lastname: Anzulewicz
authors.affiliation: Harimata Sp. z.o.o., Kraków, Poland.
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Sobota
authors.affiliation: University of Strathclyde, Glasgow, UK.
authors.forename: Jonathan T
authors.initials: JT
authors.lastname: Delafield-Butt
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder / Q000175:diagnosis* / Q000503:physiopathology* / Q000523:psychology; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D034201:Computers, Handheld; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D042241:Early Diagnosis; D005260:Female; D005868:Gestures; D006801:Humans; D000069550:Machine Learning; D008297:Male; D011597:Psychomotor Performance / Q000502:physiology*; D018910:Video Games