Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments

Automatic detection of ADHD and ASD from expressive behaviour in RGBD data

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts who employ standard questionnaires and look for certain behavioural markers through manual observation. Such methods for their diagnosis are not only subjective, difficult to repeat, and costly but also extremely time consuming. In this work, we present a novel methodology to aid diagnostic predictions about the presence/absence of ADHD and ASD by automatic visual analysis of a person's behaviour. To do so, we conduct the questionnaires in a computer-mediated way while recording participants with modern RGBD (Colour+Depth) sensors. In contrast to previous automatic approaches which have focussed only on detecting certain behavioural markers, our approach provides a fully automatic end-to-end system to directly predict ADHD and ASD in adults. Using state of the art facial expression analysis based on Dynamic Deep Learning and 3D analysis of behaviour, we attain classification rates of 96% for Controls vs Condition (ADHD/ASD) groups and 94% for Comorbid (ADHD+ASD) vs ASD only group. We show that our system is a potentially useful time saving contribution to the clinical diagnosis of ADHD and ASD

A New Perspective on Assessing Cognition in Children through Estimating Shared Intentionality

This theoretical article aims to create a conceptual framework for future research on digital methods for assessing cognition in children through estimating shared intentionality, different from assessing through behavioral markers. It shows the new assessing paradigm based directly on the evaluation of parent-child interaction exchanges (protoconversation), allowing early monitoring of children’s developmental trajectories. This literature analysis attempts to understand how cognition is related to emotions in interpersonal dynamics and whether assessing these dynamics shows cognitive abilities in children. The first part discusses infants’ unexpected achievements, observing the literature about children’s development. The analysis supposes that due to the caregiver’s help under emotional arousal, newborns’ intentionality could appear even before it is possible for children’s intention to occur. The emotional bond evokes intentionality in neonates. Therefore, they can manifest unexpected achievements while performing them with caregivers. This outcome shows an appearance of protoconversation in adult-children dyads through shared intentionality. The article presents experimental data of other studies that extend our knowledge about human cognition by showing an increase of coordinated neuronal activities and the acquisition of new knowledge by subjects in the absence of sensory cues. This highlights the contribution of interpersonal interaction to gain cognition, discussed already by Vygotsky. The current theoretical study hypothesizes that if shared intentionality promotes cognition from the onset, this interaction modality can also facilitate cognition in older children. Therefore in the second step, the current article analyzes empirical data of recent studies that reported meaningful interaction in mother-infant dyads without sensory cues. It discusses whether an unbiased digital assessment of the interaction ability of children is possible before the age when the typical developmental trajectory implies verbal communication. The article develops knowledge for a digital assessment that can measure the extent of children’s ability to acquire knowledge through protoconversation. This specific assessment can signalize the lack of communication ability in children even when the typical trajectory of peers’ development does not imply verbal communication.publishersversionPeer reviewe