3,125 research outputs found

    Computational Analysis of Developmental Disorders in Children

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    Early developmental disorders are common in children between the ages of 3 through 17. These developmental disorders begin at early ages and affect the day-to-day activities of children. These disorders also impact the growth and lifestyle of children. Most of the time these developmental disorders co-exist in children. The main focus of our research lies in Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, Deletion syndrome (22q) and their co-occurrences. Most child psychologists and pediatricians diagnose these disorders in children through parent-based surveys. Our research uses three different parent-based reports: (1) Autism Diagnostic Interview (ADI), (2) Behavioral Assessment Schedule for Children (BASC), and (3) Vineland Adaptive Behavior Scales. These reports are questionnaires filled by parents under the inspection of certified professionals. These examinations require substantial amount of time and yield results after at least 13 months of wait time; hence, there is a pressing need to expedite the disorder detection process. Here, we address this challenge by utilizing machine learning techniques. We utilize Machine learning to parent-reviews to help understand the relevance and importance of parental assessments in diagnosing these disorders. Furthermore, we study the co-occurrence of these disorders and identify their indicators in parental-surveys using a variety of machine learning techniques. Our main objective is to determine whether one can accurately predict the occurrence of these disorders

    From early markers to neuro-developmental mechanisms of autism

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    A fast growing field, the study of infants at risk because of having an older sibling with autism (i.e. infant sibs) aims to identify the earliest signs of this disorder, which would allow for earlier diagnosis and intervention. More importantly, we argue, these studies offer the opportunity to validate existing neuro-developmental models of autism against experimental evidence. Although autism is mainly seen as a disorder of social interaction and communication, emerging early markers do not exclusively reflect impairments of the “social brain”. Evidence for atypical development of sensory and attentional systems highlight the need to move away from localized deficits to models suggesting brain-wide involvement in autism pathology. We discuss the implications infant sibs findings have for future work into the biology of autism and the development of interventions

    Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

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    ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study

    Guidelines to design tangible tabletop activities for children with attention deficit hyperactivity disorder

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    Attention deficit hyperactivity disorder is one of the most frequent neurodevelopmental disorders among children. In spite of this, there is a lack of HCI research specifically devoted to these children. This paper describes efforts to transfer previous experience with other neurodiverse children in the field of tangible tabletops to ADHD children. The results of evaluation sessions carried out in conjunction with an ADHD association, complemented with an in-depth study of their special characteristics and needs, have led to a set of guidelines oriented to the design of tangible tabletop activities. These guidelines are mostly general and applicable to the design of any interactive application oriented to ADHD children. They are also appropriate for applications for other neurodiverse children or, in fact, any child from a more inclusive perspective

    Perceived Trends in ADHD Symptoms, Diagnosis, and Treatment in Vermont Schools

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    My research questions are: What can local school professional staff members’ perceptions and observations, together with available information on school services and de-identified school demographic data, tell us about retrospective and cross-sectional trends in ADHD symptoms, diagnosis, treatment and behavioral management in local Vermont schools? Is there higher incidence based on male gender, lower SES, or White racial classification, as found in other studies? How do local school professionals explain any perceived retrospective or cross-sectional trends in ADHD symptoms, diagnosis, and treatment in their schools? Are there any differences across school level (elementary, middle, versus high school)? In interviewing twelve school staff members, I found that local Vermont school staff believe it is a challenge differentiating symptoms and diagnosis of ADHD from anxiety. Family trauma is also a huge component to a child’s behavior which may look similar to ADHD-like symptoms and behaviors. Majority of school staff indicate that pediatricians are increasingly prescribing ADHD medications to children with little communication from the school. This is seen as frustrating and unprofessional from the school staff’s perspective as parents cannot be the single reporter and evaluator. School staff strongly feel that there is an unhealthy dependence on medication and only medication for treatment. Behavioral therapy is not used properly and not used enough at schools. There was variation by profession with the regard to school staff perception of the amount of increase in ADHD diagnosis of the younger cohort coming through elementary school, however, many school staff assert that the apparent increase in prevalence is due to parental labeling and by FlashMall \u3e pediatricians overdiagnosing ADHD and overprescribing ADHD medication

    The Effects of Technology Based Self-Monitoring Across General Education Settings for Students with Behavior Disorders

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    This dissertation consists of three articles investigating the effects of technology-based self-monitoring to decrease off-task behaviors and increase academic engagement in students who have behavior disorders. Previous literature has examined the effects of technology-based self-monitoring in special education and alternative placements. Unfortunately, there is a lack of investigation with technology-based self-monitoring in general education settings. Together these three articles will clearly determine the effects of technology-based self-monitoring in general education settings specifically for students with behavior disorders

    Comorbidity between attention deficit hyperactivity disorder and reading disabilities: implications for assessment and treatment

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    Comorbidity of attention deficit hyperactivity disorder (ADHD) and reading disabilities (RD) is greater than what would occur by chance. Considering the well-documented adverse impact of both ADHD and RD on development, the presence of both conditions may lead to particularly poor outcomes for affected people. This chapter, which reviews 43 research studies carried out in the last decade that have focused on the link between ADHD and RD, is divided into two broad nuclei of contents. First, studies are described that contribute information about characteristics of the comorbid phenotype. Second, studies related to procedures directed toward evaluation and intervention in this problem are analyzed. The review carried out does not make it possible to extract definitive results on the exact nature of ADHD and RD comorbidity or, even less, reach conclusions about its causes. However, the literature-based evidence shows a cognitive profile of ADHD+RD characterized by failure of various functions that can produce more severe functional deficits and worse neuropsychological, academic, and behavioral outcomes. Furthermore, the analysis of the set of results from the studies shows a limited efficacy of pharmacological and psychopedagogical treatments, and highlights the need for continued research on this topic. From a clinical and educational standpoint, the conclusions derived from this review underline the importance of performing an exhaustive evaluation of children and adolescents with symptoms of ADHD and/or RD, in order to be able to plan interventions with greater possibilities of success in each cas

    Computer screenshot classification for boosting ADHD productivity in a VR environment

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    Individuals with ADHD face significant challenges in their daily lives due to difficulties with attention, hyperactivity, and impulsivity. These challenges are especially pronounced in the workplace or educational settings, where the ability to sustain attention and manage time effectively is crucial for success. Virtual reality (VR) software has emerged as a promising tool for improving productivity in individuals with ADHD. However, the effectiveness of such software depends on the identification of potential distractions and timely intervention. The proposed computer screenshot classification approach addresses this need by providing a means for identifying and analyzing potential distractions within VR software. By integrating Convolutional Neural Networks (CNNs), Optical Character Recognition (OCR), and Natural Language Processing (NLP), the proposed approach can accurately classify screenshots and extract features, facilitating the identification of distractions and enabling timely intervention to minimize their impact on productivity. The implications of this research are significant, as ADHD affects a substantial portion of the population and has a significant impact on productivity and quality of life. By providing a novel approach for studying, detecting, and enhancing productivity, this research has the potential to improve outcomes for individuals with ADHD and increase the efficiency and effectiveness of workplaces and educational settings. Moreover, the proposed approach holds promise for wider applicability to other productivity studies involving computer users, where the classification of screenshots and feature extraction play a crucial role in discerning behavioral patterns.Les persones amb TDAH s’enfronten a reptes importants en la seva vida diària a causa de les dificultats d’atenció, hiperactivitat i impulsivitat. Aquests reptes són especialment pronunciats al lloc de treball o en entorns educatius, on la capacitat de mantenir l’atenció i gestionar el temps de manera eficaç és crucial per a l’èxit. El software de realitat virtual (RV) s’ha revelat com a eina prometedora per millorar la productivitat de les persones amb TDAH. Tanmateix, l’eficàcia del software esmentat depèn de la identificació de distraccions potencials i de la intervenció oportuna. L’enfocament de classificació de captures de pantalla d’ordinador proposat aborda aquesta necessitat proporcionant un mitjà per identificar i analitzar les distraccions potencials dins del programari de RV. Mitjançant la integració de xarxes neuronals convolucionals (CNN), el reconeixement òptic de caràcters (OCR) i el processament del llenguatge natural (NLP), l’enfocament proposat pot classificar amb precisió les captures de pantalla i extreure’n característiques, facilitant la identificació de les distraccions i permetent una intervenció oportuna per minimitzar-ne l’impacte en la productivitat. Les implicacions d’aquesta investigació són importants, ja que el TDAH afecta una part substancial de la població i té un impacte significatiu a la productivitat i la qualitat de vida. En proporcionar un enfocament nou per estudiar, detectar i millorar la productivitat, aquesta investigació té el potencial de millorar els resultats per a les persones amb TDAH i augmentar l’eficiència i l’eficàcia dels llocs de treball i els entorns educatius. A més, l’enfocament proposat promet una aplicabilitat més gran a altres estudis de productivitat en què participin usuaris d’ordinadors, en què la classificació de captures de pantalla i l’extracció de característiques tenen un paper crucial a l’hora de discernir patrons de comportament

    Construct validity and development of local norms in the assessment of ADHD.

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    A pilot study was performed to determine the validity of on-task behavior and locally developed attention tasks# to assist in the identification of children with ADHD. Subjects were third grade students in the Hampton City Public Schools. Means and standard deviations were computed for time-on-task as well as number correct and number committed for each of five separate attention tasks. A correlation analysis was performed to compare results of attention tasks with each other as well as with the Abbreviated Conners Teacher\u27s Scale (ACTS), a Hyperactivity Index, and IQ. Results were in the expected direction, although correlations with ACTS were lower than anticipated. A discussion of the results and suggestions for future studies are given
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