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

From SnappyApp to Screens in the Wild: gamifying an Attention Hyperactivity Deficit Disorder continuous performance test for public engagement and awareness

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that is characterised by three core behaviours: inattention, hyperactivity and impulsivity. It is typically thought that around 3-5% of school aged children have ADHD, with lifetime persistence for the majority. A psychometric Continuous Performance Test (CPT) had recently been incorporated into an interactive smartphone application (App), SnappyApp, to allow the measurement of the three ADHD symptom domains. SnappyApp presents a sequence of letters of the alphabet in a pseudo-random manner with responses via the device’s touch screen. Following a pilot test in the general population where the CPT showed sensitivity to ADHD-related symptoms (self-reported impulsive behaviour related to CPT measures), a new project was begun to convert the App into a game Attention Grabber based on the functionality of the test, focussing on the attention and impulsivity domains. The Screens in the Wild (SITW) platform is in the process of being employed for public engagement in awareness about ADHD through interactive technology. SITW has deployed a network of four public touch-screens in urban places. Each of the four nodes has a large (46 inch) display, a camera, a microphone and a speaker. The SnappyApp web-app was translated for presentation on to the SITW platform. The browser-based App was redesigned, with the input of a commercial graphics design company, based on an initial proof-of-concept whereby the original App was reprogrammed to present sequences of graphical objects (fruit) and to introduce further engagement features including animations. A shortened video about Adult ADHD and a brief questionnaire were incorporated to form a stand-alone edutainment package. The earlier design and user testing of SnappyApp is briefly described and details are then provided of the process of gamification to produce Attention Grabber. An evaluation process is described whereby awareness of ADHD and its related symptoms are to be probed. In general, finding out whether and how people engage with interactive screen technology can help in the design of future public engagement and health promotion activities. Ethical considerations are discussed, since public access to this kind of game could potentially raise health anxiety related to self-interpretation of game performance. This risk is balanced with the need to provide health information

Evaluating a public display installation with game and video to raise awareness of Attention Deficit Hyperactivity Disorder

Networked Urban Screens offer new possibilities for public health education and awareness. An information video about Attention Deficit Hyperactivity Disorder (ADHD) was combined with a custom browser-based video game and successfully deployed on an existing research platform, Screens in the Wild (SitW). The SitW platform consists of 46-in. touchscreen or interactive displays, a camera, a microphone and a speaker, deployed at four urban locations in England. Details of the platform and software implementation of the multimedia content are presented. The game was based on a psychometric continuous performance test. In the gamified version of the test, players receive a score for correctly selected target stimuli, points being awarded in proportion to reaction time and penalties for missed or incorrect selections. High scores are shared between locations. Questions were embedded to probe self-awareness about ‘attention span’ in relation to playing the game, awareness of ADHD and Adult ADHD and increase in knowledge from the video. Results are presented on the level of public engagement with the game and video, deduced from play statistics, answers to the questions and scores obtained across the screen locations. Awareness of Adult ADHD specifically was similar to ADHD in general and knowledge increased overall for 93 % of video viewers. Furthermore, ratings of knowledge of Adult ADHD correlated positively with ADHD in general and positively with knowledge gain. Average scores varied amongst the sites but there was no significant correlation of question ratings with score. The challenge of interpreting user results from unsupervised platforms is discussed

Anxiety in high functioning children with autism

High functioning children with autism were compared to two control groups on measures of anxiety and social worries. Comparison control groups consisted of children with expressive language disorder and typically developing children. Each group consisted of 15 children between the ages of 8 and 12 years and were matched for age and gender. Children with autism were found to be most anxious on both measures. High anxiety subscale scores for the autism group were separation anxiety and obsessive-compulsive disorder. Possible explanations for higher levels of anxiety in high functioning children with autism were explored. The groups were compared on measures of theory of mind, recognition and expression of emotion, communication and socialisation. The children with autism performed significantly worse than both control groups on the measure of socialisation. On the measures of theory of mind, recognition of emotion and communication skills, however, the children with autism did as well as children with expressive language disorder. Impairments in social abilities are, therefore, highlighted as possible factors contributing to anxiety in high functioning children with autism. Social anxiety was also found to correlate negatively with communication ability for the autism group. This is the first study to provide quantitative data on anxiety in children with autism. These findings are discussed within the context of theories of autism and anxiety in the general population of children. The clinical implications of these findings are also noted and suggestions for future research are made

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