Picture of sea vessel plough through rough maritime conditions

Innovations in marine technology, pioneered through Open Access research...

Strathprints makes available scholarly Open Access content by researchers in the Department of Naval Architecture, Ocean & Marine Engineering based within the Faculty of Engineering.

Research here explores the potential of marine renewables, such as offshore wind, current and wave energy devices to promote the delivery of diverse energy sources. Expertise in offshore hydrodynamics in offshore structures also informs innovations within the oil and gas industries. But as a world-leading centre of marine technology, the Department is recognised as the leading authority in all areas related to maritime safety, such as resilience engineering, collision avoidance and risk-based ship design. Techniques to support sustainability vessel life cycle management is a key research focus.

Explore the Open Access research of the Department of Naval Architecture, Ocean & Marine Engineering. Or explore all of Strathclyde's Open Access research...

Motor differences identify children with autism engaged in iPad gameplay

Anzulewicz, Ania and Sobota, Krzysiek and Ferrara, Maria and Delafield-Butt, Jonathon T. (2016) Motor differences identify children with autism engaged in iPad gameplay. In: International Meeting for Autism Research, 2016-05-11 - 2016-05-14, Baltimore Convention Center.

[img]
Preview
Text (Anzulewicz-etal-IMFAR2016-Motor-differences-identify-children-with-autism)
Anzulewicz_etal_IMFAR2016_Motor_differences_identify_children_with_autism.pdf
Accepted Author Manuscript

Download (7MB) | Preview

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.