Picture of offices in the City of London

Open Access research that is better understanding work in the global economy...

Strathprints makes available scholarly Open Access content by researchers in the Department of Work, Employment & Organisation based within Strathclyde Business School.

Better understanding the nature of work and labour within the globalised political economy is a focus of the 'Work, Labour & Globalisation Research Group'. This involves researching the effects of new forms of labour, its transnational character and the gendered aspects of contemporary migration. A Scottish perspective is provided by the Scottish Centre for Employment Research (SCER). But the research specialisms of the Department of Work, Employment & Organisation go beyond this to also include front-line service work, leadership, the implications of new technologies at work, regulation of employment relations and workplace innovation.

Explore the Open Access research of the Department of Work, Employment & Organisation. 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.