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.

The article presents a research study on recognizing therapy progress among children with autism spectrum disorder. The progress is recognized on the basis of behavioural data gathered via five specially designed tablet games. Over 180 distinct parameters are calculated on the basis of raw data delivered via the game flow and tablet sensors -i.e. touch screen, accelerometer and gyroscope. The results obtained confirm the possibility of recognizing progress in particular areas of development. The recognition accuracy exceeds 80%. Moreover, the study identifies a subset of parameters which appear to be better predictors of therapy progress than others. The proposed method -consisting of data recording, parameter calculation formulas and prediction models -might be implemented in a tool to support both therapists and parents of autistic children. Such a tool might be used to monitor the course of the therapy, modify it and report its results.Autism is a complex developmental disorder that influences the ability to communicate and learn. Autism is nowadays a growing challenge, as the number of children diagnosed with autism is rising worldwide 1 . The disorder exhibits a spectrum of symptoms that might range from mild to severe in a particular case, varying from skill to skill and from child to child. This makes diagnosis and therapy progress evaluation difficult and at the same time crucial for the effectiveness of the therapy. Early identification and proper therapy translates into a greater chance for preventing a person with autism from social exclusion. Therefore, any idea that might improve therapeutic practice is worth investigating. This paper presents one such idea, which incorporates the analysis of behavioural characteristics observed in autistic children while they play specially designed tablet games. This interdisciplinary study combines computer science and special pedagogy, by applying computer technologies and machine learning methods in the process of therapy for autistic children.Computer technologies may support both the diagnosis of autism and related therapy, although most solutions so far refer to the process of therapy. There are numerous applications designed for individuals with autism. These applications focus on particular issues by teaching specific skills-e.g. expressing needs, learning certain behaviours 2 , improving verbal communication, answering questions, interacting with other people in typical situations 3 , recognizing and expressing emotions 4-6 . These solutions take advantage of the fact, that children with autism are usually enthusiastic about tasks supported by computer technology, which offers a predictable framework without causing stress 2 .Another group of tools is designed for therapists. One of the most commonly used solutions are computer versions of standardized questionnaires that evaluate an individual's state 7 . Another popular way of supporting the therapy is to provide the experts with video recordings of the children's behaviour. However, there are also some...

Social sciences researchers emphasize that new technologies can overcome the limitations of small and homogenous samples. In research on early language development, which often uses parental reports, taking the testing online might be particularly compelling. Due to logistical limitations, previous studies on bilingual children have explored the language development trajectories in general (e.g., by including few and largely set apart timepoints), or focused on small, homogeneous samples. The present study protocol presents a new, on-going study which uses new technologies to collect longitudinal data continuously from parents of multilingual, bilingual, and monolingual children. Our primary aim is to establish the developmental trajectories in Polish-British English and Polish-Norwegian bilingual children and Polish monolingual children aged 0–3 years with the use of mobile and web-based applications. These tools allow parents to report their children’s language development as it progresses, and allow us to characterize children’s performance in each language (the age of reaching particular language milestones). The project’s novelty rests on its use of mobile technologies to characterize the bilingual and monolingual developmental trajectory from the very first words to broader vocabulary and multiword combinations.

Purpose The purpose of this paper is to answer the question whether it is possible to recognise the gender of a web browser user on the basis of keystroke dynamics and mouse movements. Design/methodology/approach An experiment was organised in order to track mouse and keyboard usage using a special web browser plug-in. After collecting the data, a number of parameters describing the users’ keystrokes, mouse movements and clicks were calculated for each data sample. Then several machine learning methods were used to verify the stated research question. Findings The experiment showed that it is possible to recognise males and females on the basis of behavioural characteristics with an accuracy exceeding 70 per cent. The best results were obtained while using Bayesian networks. Research limitations/implications The first limitation of the study was the restricted contextual information, i.e. neither the type of web page browsed nor the user activity was taken into account. Another is the narrow scope of the respondent group. Future work should focus on gathering data from more users covering a wider age range and should consider the context. Practical implications Automatic gender recognition could be used in profiling a user to create personalised websites or as an additional feature in automatic identification for security reasons. It might be also considered as a confirmation of declared gender in web-based surveys. Social implications As not all users perceive personalised ads and websites as beneficial, this application requires the analysis of a user perspective to provide value to the consumer without privacy violation. Originality/value Behavioural characteristics, such as mouse movements and keystroke dynamics, have already been used for user authentication and emotion recognition, but applying these data to gender recognition is an original idea.

IntroductionRecent evidence suggests an underlying movement disruption may be a core component of autism spectrum disorder (ASD) and a new, accessible early biomarker. Mobile smart technologies such as iPads contain inertial movement and touch screen sensors capable of recording subsecond movement patterns during gameplay. A previous pilot study employed machine learning analysis of motor patterns recorded from children 3–5 years old. It identified those with ASD from age-matched and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom.Methods and analysisThis is a phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies guidelines. Three cohorts are investigated: children typically developing (TD); children with a clinical diagnosis of ASD and children with a diagnosis of another neurodevelopmental disorder (OND) that is not ASD. The study will be completed in Glasgow, UK and Gothenburg, Sweden. The recruitment target is 760 children (280 TD, 280 ASD and 200 OND). Children play two games on the iPad then a third party data acquisition and analysis algorithm (Play.Care, Harimata) will classify the data as positively or negatively associated with ASD. The results are blind until data collection is complete, when the algorithm’s classification will be compared against medical diagnosis. Furthermore, parents of participants in the ASD and OND groups will complete three questionnaires: Strengths and Difficulties Questionnaire; Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations Questionnaire and the Adaptive Behavioural Assessment System-3 or Vineland Adaptive Behavior Scales-II. The primary outcome measure is sensitivity and specificity of Play.Care to differentiate ASD children from TD children. Secondary outcomes measures include the accuracy of Play.Care to differentiate ASD children from OND children.Ethics and disseminationThis study was approved by the West of Scotland Research Ethics Service Committee 3 and the University of Strathclyde Ethics Committee. Results will be disseminated in peer-reviewed publications and at international scientific conferences.Trial registration numberNCT03438994