Assessment of Sensory Abnormalities in Children with ASD Using Virtual Reality

Sensory abnormality is one of the important characteristics of Autism Spectrum Disorder(ASD). A large number of studies suggest a strong connection between sensory abnormalities and ASD. Therefore, it is important to assess sensory abnormalities for diagnosis and intervention of ASD. In this thesis study, we aim to develop a virtual reality system that can measure sensory abnormalities in adolescents with ASD. Sensory abnormalities can affect all five senses: touch, vision, smell, taste and auditory. However, in this study, we focus on assessing sensory abnormality in vision and touch sensory processing. Twelve adolescents with ASD and 12 typically developing (TD) adolescents aged 11-17 years participated in the study. Participants were assigned a task in which they interacted with the virtual reality system. The system recorded participants’ behavior and their response to the virtual environment in the real time while they completed the task. We defined four measurements to analyze the behavior of the participants. With the help of the defined measurements, we found some significant differences in the way participants with ASD and TD participants interacted with the virtual environment. Participants also filled a commonly used standard psychological assessment questionnaire called Adult/Adolescent Sensory Profile (AASP). Strong correlations between some of the scores in the questionnaire and their response to the virtual environment were also observed. Therefore, this pilot study supports the use of technology to assess sensory abnormalities and shows that further research into the development of such technology is essential

The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality

Many symptoms of the autism spectrum disorder (ASD) are evident in early infancy, but ASD is usually diagnosed much later by procedures lacking objective measurements. It is necessary to anticipate the identification of ASD by improving the objectivity of the procedure and the use of ecological settings. In this context, atypical motor skills are reaching consensus as a promising ASD biomarker, regardless of the level of symptom severity. This study aimed to assess differences in the whole-body motor skills between 20 children with ASD and 20 children with typical development during the execution of three tasks resembling regular activities presented in virtual reality. The virtual tasks asked to perform precise and goal-directed actions with different limbs vary in their degree of freedom of movement. Parametric and non-parametric statistical methods were applied to analyze differences in children's motor skills. The findings endorsed the hypothesis that when particular goal-directed movements are required, the type of action could modulate the presence of motor abnormalities in ASD. In particular, the ASD motor abnormalities emerged in the task requiring to take with the upper limbs goal-directed actions with low degree of freedom. The motor abnormalities covered (1) the body part mainly involved in the action, and (2) further body parts not directly involved in the movement. Findings were discussed against the background of atypical prospective control of movements and visuomotor discoordination in ASD. These findings contribute to advance the understanding of motor skills in ASD while deepening ecological and objective assessment procedures based on VR

Neuroaffirmative Approaches to Extended Reality: Empowering Individuals with Autism Spectrum Condition through Immersive Learning Environments

Traditional teaching and working environments often prioritize extroverted qualities, disadvantaging individuals with conditions that impact social engagement, such as autism spectrum condition (ASC). These individuals usually thrive in calmer, low-key learning environments but face challenges in lecture-style classes, and traditional office environments leading to marginalization in academic and professional settings. This study explores the neuroaffirming potential of extended reality (XR) in creating immersive learning and working environments tailored to the unique needs of individuals with ASC. By focusing on four key factors—indirect social engagement, digital communication preferences, sensory sensitivity, and avatar-based communication—XR technologies can provide a supportive and accommodating environment for those with sensory processing disorders (SPD). As the metaverse and virtual reality (VR) technology advances, education and industry can harness social VR to prepare students for a future of work defined by virtual collaboration. This research investigates the transformative role of XR and the metaverse in promoting a more inclusive educational and professional landscape by adapting environments to empower individuals with ASC, enabling them to reach their full potential in a neuroaffirmative manner

Sensorimotor Differences in Autism Spectrum Disorder: An evaluation of potential mechanisms.

This thesis examined the aetiology of sensorimotor impairments in Autism Spectrum Disorder: a neurodevelopmental condition that affects an individual’s socio-behavioural preferences, personal independence, and quality of life. Issues relating to clumsiness and movement coordination are common features of autism that contribute to wide-ranging daily living difficulties. However, these characteristics are relatively understudied and there is an absence of evidence-based practical interventions. To pave the way for new, scientifically-focused programmes, a series of studies investigated the mechanistic underpinnings of sensorimotor differences in autism. Following a targeted review of previous research, study one explored links between autistic-like traits and numerous conceptually-significant movement control functions. Eye-tracking analyses were integrated with force transducers and motion capture technology to examine how participants interacted with uncertain lifting objects. Upon identifying a link between autistic-like traits and context-sensitive predictive action control, study two replicated these procedures with a sample of clinically-diagnosed participants. Results illustrated that autistic people are able to use predictions to guide object interactions, but that uncertainty-related adjustments in sensorimotor integration are atypical. Such findings were advanced within a novel virtual-reality paradigm in study three, which systematically manipulated environmental uncertainty during naturalistic interception actions. Here, data supported proposals that precision weighting functions are aberrant in autistic people, and suggested that these individuals have difficulties with processing volatile sensory information. These difficulties were not alleviated by the experimental provision of explicit contextual cues in study four. Together, these studies implicate the role of implicit neuromodulatory mechanisms that regulate dynamic sensorimotor behaviours. Results support the development of evidence-based programmes that ‘make the world more predictable’ for autistic people, with various theoretical and practical implications presented. Possible applications of these findings are discussed in relation to recent multi-disciplinary research and conceptual advances in the field, which could help improve daily living skills and functional quality of life.Economic and Social Research Council (ESRC

Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder

Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR). Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding. Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational

Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review

With the rising prevalence of autism diagnoses, it is essential for research to understand how to leverage technology to support the diverse nature of autistic traits. While traditional interventions focused on technology for medical cure and rehabilitation, recent research aims to understand how technology can accommodate each unique situation in an efficient and engaging way. Extended reality (XR) technology has been shown to be effective in improving attention in autistic users given that it is more engaging and motivating than other traditional mediums. Here, we conducted a systematic review of 59 research articles that explored the role of attention in XR interventions for autistic users. We systematically analyzed demographics, study design and findings, including autism screening and attention measurement methods. Furthermore, given methodological inconsistencies in the literature, we systematically synthesize methods and protocols including screening tools, physiological and behavioral cues of autism and XR tasks. While there is substantial evidence for the effectiveness of using XR in attention-based interventions for autism to support autistic traits, we have identified three principal research gaps that provide promising research directions to examine how autistic populations interact with XR. First, our findings highlight the disproportionate geographic locations of autism studies and underrepresentation of autistic adults, evidence of gender disparity, and presence of individuals diagnosed with co-occurring conditions across studies. Second, many studies used an assortment of standardized and novel tasks and self-report assessments with limited tested reliability. Lastly, the research lacks evidence of performance maintenance and transferability.Comment: [Accepted version] K. Wang, S. J. Julier and Y. Cho, "Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review," in IEEE Access, vol. 10, pp. 15574-15593, 2022, doi: 10.1109/ACCESS.2022.314772

Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review

The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed

Introversion And Autism: A Conceptual Exploration Of The Placement Of Introversion On The Autism Spectrum

The conceptualization of the personality construct of introversion has been problematic since the term\u27s inception due to the complexity and seemingly self-contradictory nature of the collection of attributes of which it is comprised. To advance the understanding of introversion, I propose that it is a continuous segment of the non-clinical part of the autism spectrum, and that it is not the same as the inverse of extraversion. When introversion and autism are placed on the same continuum, the nature of the relationship of the traits becomes more apparent, and new possibilities are available for exploration of both autism and introversion. This review of literature traces the origins and development of the concept of introversion and places it on the autism spectrum, demonstrating the apparent synonymous nature of the traits despite varying degrees of severity in expression. The current factorial structure of introversion demonstrates how autistic features interact to produce the personality dimension. Other factors, including genetic predisposition, relationships to the clinical and non-clinical symptoms of schizophrenia spectrum expression, and neurological findings that support the correlation will be considered. Finally, suggestions for future research and possible theoretical and empirical implications and applications are explored