![]() ![]() Using the touch data collected and the tablet kinetic information provided by the device sensors, we computed a set of features representing the participant’s motor behavior. The repeated bubble is designed to assess the tendency to return to the same bubble (repetitive behavior) versus exploring other bubbles.Ĭaregivers were asked to hold their child on their lap, and the child was encouraged to pop the bubbles independently before the analyzed data was recorded for 20 seconds. When the bubble is popped, it appears again (same cartoon character and color) from the bottom of the same lane, otherwise, a random one appears after the bubble exits the screen from the top. Any time a bubble is touched, the bubble pops, making a distinct popping sound releasing a cartoon animal character inside the bubble. The game is composed of 5 vertical tracks with bubbles appearing from the bottom and moving upwards. The game was presented on an iPad placed on a tripod around 50 cm from the participant. The bubble-popping game was delivered at a clinic following a well-child visit with a pediatrician. Sam Perochon led the analysis of the data for this study. Finally, we examined whether motor digital phenotypes are correlated with standardized measures of cognitive, language, and motor abilities, as well as the level of autism-related behaviors. We also examined whether having co-occurring ADHD affected motor performance. In this study, we hypothesized that autistic children would have a distinct performance on the bubble-popping game compared to neurotypical children. ![]() In previously published work with the app, we showed that computer vision analysis and machine learning can be used to automatically quantify a wide range of autism signs, including differences in gaze, attention, facial expressions and dynamics, and head movements. The app displays developmentally appropriate and strategically designed movies on a smartphone or tablet while the camera in the device records the child’s behavioral responses to the stimuli. The bubble-popping game developed by our team, co-led by Guillermo Sapiro and me, is one part of a mobile application (app) - SenseToKnow - that is designed to detect early signs of autism. These data provide promising ways to identify and quantify an autism motor signature and characterize the nature of motor impairments in autism. The development of miniaturized inertial sensors and wearable sensors, and the ubiquity of mobile devices such as tablets and smartphones have allowed unprecedented access to massive multimodal data that have been used to characterize motor behavior. This study analyzed the use of a tablet-based bubble-popping game to assess such early visual-motor skills, as it requires the coordination of a dynamic visual stimulus with a motor response involving touch. Studies have found that autistic people often struggle with tasks that involve visual-motor integration, which can have an impact on the development of social skills. Motor impairments are often one of the earliest reported signs associated with autism, and thus early assessment of motor skills could be an important component of a screening battery for autism. Early detection of autism provides an opportunity for early intervention, which can improve developmental trajectories and strengthen social, language, cognitive, and motor competencies during a period of heightened brain plasticity. ![]()
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