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.

Increased Force Variability Is Associated with Altered Modulation of the Motorneuron Pool Activity in Autism Spectrum Disorder (ASD)

“…Our findings were consistent with prior studies showing increased variability of manual motor output in ASD during precision gripping [16,17,39], writing [13], object lifting [40,41] and the use of simple finger gestures [44]. As the task of isometric index finger abduction only involves the FDI muscle, our study suggests that failure to precisely adjust force control and motor output in response to visual feedback is evidenced even when actions are restricted to a single muscle group as opposed to requiring coordination across different effectors or muscles.…”

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

“…Abnormalities in postural stability have been documented using an electronic balance board (Nintendo Wii), which showed that autistic participants had more difficulty maintaining balance (Travers, Powell, Klinger, & Klinger, 2013). Discriminant analysis has shown that autistic children can be sensitively and specifically classified according to the speed, force and pressure of their finger movements on a tablet screen (Anzulewicz, Sobota, & Delafield-Butt, 2016); machine learning was also seen to correctly identify them by the kinematics of reach-to-drop (Crippa et al, 2015) and reachto-throw tasks (Perego, Forti, Crippa, Valli, & Reni, 2009). Other studies of kinematics in very basic arm movements and reaching and grasping reveal differences between autistic and TD participants (Campione, Piazza, Villa, & Molteni, 2016;Cook, Blakemore, & Press, 2013).…”

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

“…Third, there remains a lack of clear understanding of the technique that is being used. A number of studies used multiple algorithm approaches and report on the highest predictive value [32,33,36,37,43,44,48,49]. Before arguing on the best algorithm to use, it would be important to understand why there are such differences in the results and the reason as to which approach would be most appropriate depending on the characteristics of the dataset and what sort of an output one is trying to achieve.…”

“…Therefore, researchers have attempted to capture differences in movement patterns to use as a distinctive characteristic of ASD [41,42]. Studies by Li et al [43] and Anzulewicz et al [44], each used imitation based on observation and gesture patterns using smart tablet devices to detect kinematic parameters to use for classifying between ASD and non-ASD.…”