Search

Autism spectrum disorders (ASDs) are characterized by a lack of social communication skills. According to studies, people with ASD may be unable to notice important nonverbal cues that are normally used to facilitate social connection. Children with autism also have difficulty sustaining their selective attention during learning. The purpose of this paper to develop an Augmented Reality application that suitable for autism children to stay focus in learning. In addition, the study will measure the perceptions and user responses to AR applications This paper proposes a flashcard to assist teachers in guiding ASD students through the process of learning about wudhu. The flashcard, in combination with video, animation, and Augmented Reality, helps children with ASD maintain their focus. The respondents of this study were autism children and special education teachers from Ar Rayyan Quran Academy, Sentul, Kuala Lumpur. The main respondents were te. (10).students with autism syndrome and tw. (2).special need education teachers as a subject matter expert. Respondents were given the application of the AR Wudhu and then underwent pre and post test to see its effectiveness. findings obtained through pre-test and post-test, analyses were performed using the Wilcoxon T test, indicating that after utilizing the built AR Wudhu application, children's understanding of wudhu improved significantly. [ABSTRACT FROM AUTHOR]

In this position paper, we argue for designing autistic social media for thriving neurodiverse online communities. We discuss emerging work in the field of Human-Computer Interaction around autism and social media design, and explore how a Banal Autistic Design approach can lead to a less deficit-focused design paradigm by conducting a Found Footage Autoethnography, a method we introduce as an autoethnography variant suited to autistic embodied ways of knowing. Finally, we juxtapose our design insights with existing design guidelines for autistic social media, and argue for design for customisation, anti-normativity, non-prescripitiveness, granular publics, and interest-centricity. [ABSTRACT FROM AUTHOR]

Adults with autism spectrum disorder (ASD) face daily challenges dealing with stress, for example, at work, or in public spaces, which can be overwhelming in terms of coping with sensory overload. Technological interventions have been created in order to attempt to counter symptoms of stress, through improving cognition and functioning of target users. Mobile devices, virtual reality (VR) and augmented reality (AR) applications have demonstrated the ability to support and improve clinical practices through sensory based technologies. In this paper, we present the initial phase of research concerning an online survey conducted with over 200 autistic participants and caregivers. We report on the identification of currently used technological preferences, triggers associated with stress, issues regarding sensitivity and perception, the needs of caregivers, as well as the perceived potential for new technologies. The study has identified that sensitivity to sound and light remain key stress triggers for those on the autism spectrum. Headphones and mobile devices were considered to be suitable methods of accessing multi-sensory stimuli to help manage stress. Augmented Reality and VR were identified by autistic participants as technologies that might aid in the reduction of unpredictable stress-related events. The insights gained from the research will inform future research regarding the design of technological self-interventions for stress management in autistic adults. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder also called as autism is a developmental illness with no complete treatment that is marked by issues with speech and communication. Autism is a complex, difficult and permanent developmental disorder that is characterized by issues with repetitive behavior, nonverbal communication and lack of focus. We have proposed a methodology in this paper that will help inthe early diagnosis of ASD. We have used a dataset of 1986 patients and applied 27 features that is available on the Kaggle. Then the artificial intelligence model is prepared with the dataset obtained from Kaggle and the comparison of the result was done with the KNN,Random Forest, Decision Tree, Gradient Boosting Classifier. The accuracy obtained by the machine learning classifier was 98.17% and when it was compared with other functional classifiers such as KNN, RF, DT, SVM, Gradient Boosting, Naïve Bayes (accuracy lies between 68-93%) it was found that proposed machine learning model achieved the highestaccuracy. Hence, we can say that the proposed model provides an effective way of Autism prediction. The result of the study confirms that our proposed machinelearning model obtained the best accuracy rate amongall the different classifiers used by different authors topredict Autism. [ABSTRACT FROM AUTHOR]

According to the current statistics in the worldwide, it is estimated that one in 150 children are suffering from autism spectrum disorder, in which boys are four times more effected than girl child. Mostly the reasons are found with individual is genetic or chromosomal conditions causing the severity in ASD. It is also found that the medicine that parents consume during pregnancy is another root cause for occurrence of ASD in the early childhood. One of the general procedures for detection of ASD in children is regular visits of doctors. As of now there is no perfect medical test in particular. The child's developmental history is one of the basis to diagnose the ASD. Mostly ASD can be detected at the age of 18 months or still younger than that. The existing machine learning based detection methods relaying on few attributes that are not sufficient in confirming the better accuracy prediction analysis. This paper inflates the use of machine learning algorithms in prediction analysis with mere improvement in accuracies. The work carried out using three different algorithms i.e logistic regression, support vector machine (SVM) and decision tree. These three are selected basically due to their interdependency of attributes and also on complexity variation in the implementation. From the results it is observed that SVM has got better results with a prediction accuracy of 96%, followed by decision tree with a prediction accuracy of 92% for the chosen ASD dataset belongs to children below 3 years. However Logistic Regression is least considered due low accuracy prediction of 72%. [ABSTRACT FROM AUTHOR]

Prospective students who have been diagnosed with autism may lie along the breadth of the autism spectrum. The spectrum itself is wide, consisting of neurodivergent individuals as well as ranging from those who are non-verbal or otherwise. Generally speaking, neurodivergent individuals with autism are diagnosed with having autism spectrum disorder are classified as mild, mild-moderate, moderate, moderate-severe and severe. These classifications stretch across the autism spectrum and may also include individuals with behavioral challenges and hence are individuals with high needs, requiring elevated levels of care from those of us who are aptly experienced and neurotypical. There is little literature, particularly within the Asian region, on how engineering education increases its inclusivity, extending such programmes to prospective students who are autistic. It is unclear whether such engineering undergraduate programmes, in its current forms, may be open to those who are mild, moderate, or other classifications. Information is also scarce in terms of how engineering education as a whole may be further evolved to include such individuals. This paper attempts to share current efforts on how engineering education aims to increase inclusiveness for individuals who have been diagnosed with autism. [ABSTRACT FROM AUTHOR]

Data about the prevalence of autism worldwide diverge across different censuses. However, with the growing availability of diagnostic assessments, the number of diagnosed autistic persons has been increasing over the years and, with that, also the need for accommodations. One differentiating characteristic of autistic persons compared to those who are not is a hypo- or hypersensitive response to any sensory input, usually referred to as sensory processing disorder. Here we focus on auditory hypersensitivity. In room acoustics, and more specifically in the field of classroom acoustics, it is known that the design of indoor spaces affects speech intelligibility and comprehension for persons regardless of their neurological condition, which is essential in learning environments. For autistic persons, high levels of reverberation and background noise may lead to a sensory overload. This situation could be ameliorated with the use of assistive technology or adequate acoustic design. In this paper we present a literature review regarding the impacts of room acoustics on autistic persons, how it can be used as an accessibility tool, and propose ideas for work to be done to better identify and resolve acoustic issues for autistic persons in classrooms. [ABSTRACT FROM AUTHOR]

Early identification and diagnosis of Autism Spectrum Disorder (ASD), along with primary prevention, are crucial and may have long-term positive impacts on the lives of individuals with ASD. Research developments indicate towards current processes for early ASD evaluation that include facial expressions, body movement, eye movement monitoring, machine learning, computer vision, as well as other assessment techniques. This paper provides an examination of previous research, focusing on the stimuli, dataset, methodology, eye movement, and facial expressions for detection of ASD with the accuracy measures. Evidence suggests that combination of more than one biomarker conjunction with machine and deep learning techniques might be a useful tool for accurate and early diagnosis of autism. [ABSTRACT FROM AUTHOR]

Social robots are increasingly being used as a mediator in robot-assisted autism therapy to improve children's social and cognitive skills. Engagement is one of the key measurements used to evaluate the therapeutic interventions' effect on children. While "engagement" is broadly used, it has been challenging to find a consensus about its definition in the community. With this paper, we explore the use of a data-driven approach to investigate the extent to which a model on engagement built on one dataset transfers to another. We utilized two publicly available datasets of engagement recognition, namely PInSoRo and Qamqor datasets, with an attempt to achieve a higher accuracy taking into account the transferred knowledge. The accuracy of 83.18% was obtained on the PInSoRo dataset of child-child interactions with face and body keypoints. We have used the methodology of transfer learning to improve the classification accuracy on the Qamqor dataset. The best result obtained is a 71.89% accuracy on the Qamqor dataset. This suggests that more data with similar keypoints is needed to achieve better accuracy when utilizing transfer learning from one dataset to another dataset. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a 35% increase in the number of children requiring general anaesthetic (GA) for medical imaging, with 20% of children needing a GA to achieve successful Magnetic Resonance Imaging (MRI) of diagnostic quality (Anwar et al. 2022; Sum et al. 2019). While interventions have been introduced to help children go through MRI awake, none have comprehensively considered the feasibility of implementation, flexibility, or personalization of the intervention, nor have children been included as co-creators of the interventions. Moreover, there is a lack of research on children with neurodiverse needs. To fill this gap, this project aims to develop a patient participatory approach to investigate the paediatric patient journey, co-design and evaluate interventions, and explore the experiences of children between the ages of four and 10, including those with autism, referred to University Hospitals Dorset for MRI scans. By improving the patient experience, this research can not only enable prompt diagnosis and treatment but also affect patient attitudes, treatment compliance, and future healthseeking behaviours, ultimately improving the overall health of the population. [ABSTRACT FROM AUTHOR]

This research designed and applied 24 multi-purposeful robot activities of varying social mediation levels in a multiple-session experiment with 34 children of diverse autistic characteristics in a rehabilitation setting. This paper explores what type of robot activities can meet individual needs to bring more socio-behavioral progress and juxtaposes child characteristics to identify behavioral outcomes in each activity. This knowledge would help us to respond to the question of what activity types suit specific subgroups of Autism Spectrum Disorder (ASD). Our data analysis included coding 48.5 hours of video data for a total of 14 measures to fully capture children's activity-based socio-emotional outcomes. Overall, the activities on varying social mediation levels brought more or less positive social outcomes to all children. However, children showed some different behavioral outcomes as mediated by core autism-related and age-specific characteristics. This study provides in-depth accounts of what might be helpful in designing and applying multi-purposeful activities responsive to the diverse needs of children. [ABSTRACT FROM AUTHOR]

Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency & explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency & explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling. Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission's proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation. [ABSTRACT FROM AUTHOR]

Autism is a neurological disorder, in which affected person might be unable to talk, express, socialize. There could be many more other incapabilities with varying severities, so it is also known as autistic spectrum disorder (ASD). Autism could not be cured in general and procrastination in ASD detection could make the situation deteriorated. But situation could be make better if it is diagnosed and early intervened by appropriate therapies. The detection of autism in toddlers is done by trained medical practitioners but there are lack of trained medical practitioners available for autism detection in many countries specially developing countries. In this paper we presented a comparative study for autism detection in toddlers through various machine learning techniques. In the study we compare precesion-recall curve and accuracies of different machine learning models. We used a publicly available dataset "Autistic Spectrum Disorder Screening Data for Toddlers" for our study. [ABSTRACT FROM AUTHOR]

We present perspectives from 10 autistic Twitch streamers regarding their experiences as livestreamers and how autism uniquely colors their experiences. Livestreaming offers a social online experience distinct from in-person, face-to-face communication, where autistic people tend to encounter challenges. Our reflexive thematic analysis of interviews with 10 participants showcases autistic livestreamers' perspectives in their own words. Our findings center on the importance of having streamers establishing connections with other, sharing autistic identities, controlling a space for social interaction, personal growth, and accessibility challenges. In our discussion, we highlight the crucial value of having a medium for autistic representation, as well as design opportunities for streaming platforms to onboard autistic livestreamers and to facilitate livestreamers communication with their audience. [ABSTRACT FROM AUTHOR]

Noise sensitivity is challenging for many people and particularly prevalent for autistic people. In an effort to understand these experiences from the perspective of autistic people and the opportunities for assistive technologies to support noise-sensitive people, we analyzed the posts and responses of noise-sensitive autistic people and their allies in two online forums. These forums were intentionally chosen for their inclusion and centering of autistic people's experiences as we strove to understand this space from their perspective. Our analysis of both original discussion forum posts about noise sensitivity and comments in response to them reveal three themes: sharing experiences of noise sensitivity, managing noise sensitivity, and disclosing autism. Our work contributes a foundational understanding of the lived experiences of autistic people who are noise-sensitive and the implications for the design of assistive technology to support noise sensitivity. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a term used to describe a group of neurodevelopmental abnormalities. Differences in communication and social interaction are characteristics of these disorders. People with ASD may have limited and repetitive hobbies or behavioural patterns.ASD affects people of all races & ethnicities, cultures, and socioeconomic backgrounds. Early infancy is when ASD symptoms typically appear, usually between the ages of 12 and 24 months. Symptoms, on the other hand, could appear at any time. A severe delay in linguistic or social development could be one of the first indicators. Autism involves symptoms relating to physical movements and behaviours and speech and social difficulties. It's unclear why autism occurs. It might be caused by issues in the regions of your brain that interpret sensory information and process language. It may happen to persons of any race, nationality, or social status. The proposed study is reviewed to identify the autistic disorder and detect this ASD problem using machine learning, as well as to conclude the behavioral features of human nature during ASD. [ABSTRACT FROM AUTHOR]

Students with low-functioning autism are students who experience interaction and communication disorders and are with low intelligence levels. This disorder causes obstacles in learning in the classroom; individuals with this disorder have a difficulty understanding concepts. Geometry is a mathematical concept that is very relevant to everyday life. This study aimed to describe the profile of an autistic student's understanding of basic geometric concepts, especially on rectangles and squares. The subject in this research was an elementary school student with low-functioning autism. Data were derived from a task-based interview using an instrument measuring the understanding of the concept of geometry. The geometric concept understanding profile uses the devices of interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining. Some characteristics of autism were very perceptible in the student when he was attempting to understand the concept of geometry. The subject interpreted squares and rectangles as solid figures, naming them boxes. There were several real objects that he could imagine as squares and rectangles, but they were very limited in number. He was able to classify shapes into squares, rectangles, and shapes that were neither squares nor rectangles. He was experiencing limitations in expressing what he thought about the characteristics of the two shapes. He thought that the two shapes are similar in the sense that both are boxes and that they differ in color (referring to the pieces used herein); this was far from the correct concept. He thought that a square can also be referred to as a rectangle because the two shapes are boxes. That a box is a visualization of a square or a rectangle had been embedded in the subject's imagination. [ABSTRACT FROM AUTHOR]

Along the COVID-19 pandemic, people with autism spectrum disorders faced additional disadvantages and challenges for continuing with their learning and support therapies remotely. We have carried out research in several phases since the first wave of the pandemic, to obtain the global picture of the benefits and issues that come with online education and assistance for individuals on the spectrum. Based on our findings, we present in this poster a preliminary inclusive design that address accessibility barriers for people with autism when facing remote learning, so they can have a continuous and independent development of their learning process. [ABSTRACT FROM AUTHOR]

We interviewed 10 autistic livestreamers to understand their motivations for livestreaming on Twitch. Our participants explained that streaming helped them fulfill social desires by: supporting them in making meaningful social connections with others; giving them a safe space to practice social skills like "small talk"; and empowering them to be autistic role models and to share their true selves. This work offers an early report on how autistic individuals leverage livestreaming as a beneficial social platform while struggling with audience expectations. [ABSTRACT FROM AUTHOR]

Traditional symbol-based AAC devices impose meta-linguistic and memory demands on individuals with complex communication needs and hinder conversation partners from stimulating symbolic language in meaningful moments. This work presents a prototype application that generates situation-specific communication boards formed by a combination of descriptive, narrative, and semantic related words and phrases inferred automatically from photographs. Through semi-structured interviews with AAC professionals, we investigate how this prototype was used to support communication and language learning in naturalistic school and therapy settings. We find that the immediacy of vocabulary reduces conversation partners' workload, opens up opportunities for AAC stimulation, and facilitates symbolic understanding and sentence construction. We contribute a nuanced understanding of how vocabularies generated automatically from photographs can support individuals with complex communication needs in using and learning symbolic AAC, offering insights into the design of automatic vocabulary generation methods and interfaces to better support various scenarios of use and goals. [ABSTRACT FROM AUTHOR]

Socially Assistive Robots are effective at supporting autistic children in a variety of different therapies. Therapists can control the robots' motions and verbalizations to engage children and deliver therapeutic interventions based on their needs. We present teleoperation capabilities to support therapists in customizing therapy to their clients' needs. Specifically, we introduce a documentation sidebar that aims to prime therapists using their clients' documented needs, and a session summary report that helps therapists reflect on the session with the child. We present preliminary designs for these capabilities and describe future work to build upon them. [ABSTRACT FROM AUTHOR]

Research has recognized the importance of individual differences of children with Autism Spectrum Disorder (ASD) that require interventions to meet their heterogeneous needs. This relatively large-scale study investigates a robot-assisted autism therapy (RAAT) with 34 children with diverse forms of ASD and Attention Deficit Hyperactivity Disorder (ADHD). We conducted a multi-session study with multi-purposeful activities targeting the socio-emotional abilities of children in a rehabilitation setting. We found a number of quantitative results suggesting various autism-related and demographic differences such as diverse forms of ASD, co-occurrence of ADHD, verbal skills, and age groups. The main findings are: 1) severity of ASD forms may not predict intervention outcomes but instead the co-occurrence of ADHD with LFA diagnosis may negatively impact social smiling; 2) verbal children were more generally engaged and less aggressive with the robot than non-verbal children whose curiosity rose over sessions; and 3) younger children (3.4 y.o.) showed more affection, while older children (7-12 y.o.) were better engaged through speaking more words and having longer engagement and eye contact with the robot. [ABSTRACT FROM AUTHOR]

Research Question What guidelines and recommendations will define the best Canadian strategy to enable freedom of flying to persons living with dementia, their travel companions, the airline staff, and other passengers?. The novelty of this topic and the scarcity of academic literature led to organizing a preliminary systematic literature review on separate investigations. This combines scientific and grey literature by merging the two main concepts of dementia and the air travel experience. This study proposes netnographic methods to bring inclusiveness and innovation in focus. This internet-based data collection approach delivers many benefits, including reaching across international borders, gaining access to existing conversations, avoiding questioning biases, and generating ideas alongside participants with agility. Web scraping is used to study consumer conversations on selected social media platforms, uncovering the extensive and detailed views of people living with dementia, travel companions, and other passengers during their travel plans. Firstly, through purely observational and non-participatory netnography, the data has been collected without connecting with online communities. The initial framework was created based on the insights from the literature review, leading to identifying keywords and patterns to commence open coding. The interpretation has been made through computer-assisted analysis. Next, a secondary active and participatory netnographic approach will provide direct interaction with online communities through asynchronous online focus groups. Summary of Findings Numbers have shown that globally we are faced with a constant ageing population that interrelates with health factors. Dementia has been seen as a primary challenge prominently growing. Its incurable nature must lead to acting and planning for a projected enhanced quality of life. Findings have shown that when addressing dementia, society and people still adopts stigmatized attitudes shaping it into a disability socially constructed. The underlying lack of inclusion and accessibility results in adversities that persons living with dementia constantly encounter, astonishingly in transportation and mobility. The number of travellers with dementia will increase with the escalation of the older population, and the growing demand for dementia-friendly destinations will require augmentation in transportation to allow these target tourists to reach their locations. When travelling by air with dementia, it is fundamental to think about the whole journey, such as the planning of the trip, the time spent at the airport, the flying experience, and the post-flight. Furthermore, there is still a lack of awareness, education and training in this field. The primary keyword discussed is the concept of universality. There is a need for universal standards, a versatile terminology setting, a universal global policy, and a universal database. Key Contributions This study shines the spotlight on a group of consumers, persons living with dementia and their travel companion, who rarely get much attention as they are not representative of mass-market travellers. Accordingly, their views, needs, and preferences are underrepresented or marginalized. This research is all about accessibility and inclusiveness and stresses the need to make things accessible to each and every one. A qualitative action-oriented research approach combined with the service marketing discipline has been chosen to nurture social change through collaboration between community partners and researchers by reducing disparities and discrimination for a better quality of life. The aim is to formulate guidelines and recommendations to outline a Canadian strategy to serve this clientele appropriately and shape their experience at its best. This will provide policymakers and industries with informed alternatives while presenting individuals with information about identifying and fighting for their rights. This research's insights will not only address the barriers within the dementia context but could also yield spillover benefits by eliminating impediments to other people affected by invisible disabilities such as mental health issues, autism, and hearing disorders. Lastly, it could help address the transport industry's challenges in the post-COVID19 era. [ABSTRACT FROM AUTHOR]