Measuring the Behavioral Response to Spatial Audio within a Multi-Modal Virtual Reality Environment in Children with Autism Spectrum Disorder

Virtual Reality (VR) has been an active area of research in the development of interactive interventions for individuals with autism spectrum disorder (ASD) for over two decades. These immersive environments create a safe platform in which therapy can address the core symptoms associated with this condition. Recent advancements in spatial audio rendering techniques for VR now allow for the creation of realistic audio environments that accurately match their visual counterparts. However, reported auditory processing impairments associated with autism may affect how an individual interacts with their virtual therapy application. This study aims to investigate if these difficulties in processing audio information would directly impact how individuals with autism interact with a presented virtual spatial audio environment. Two experiments were conducted with participants diagnosed with ASD (n = 29) that compared: (1) behavioral reaction between spatialized and non-spatialized audio; and (2) the effect of background noise on participant interaction. Participants listening to binaural-based spatial audio showed higher spatial attention towards target auditory events. In addition, the amount of competing background audio was reported to influence spatial attention and interaction. These findings suggest that despite associated sensory processing difficulties, those with ASD can correctly decode the auditory cues simulated in current spatial audio rendering techniques

SoundFields: A Virtual Reality Game Designed to Address Auditory Hypersensitivity in Individuals with Autism Spectrum Disorder

Individuals with autism spectrum disorder (ASD) are characterised as having impairments in social-emotional interaction and communication, alongside displaying repetitive behaviours and interests. Additionally, they can frequently experience difficulties in processing sensory information with particular prevalence in the auditory domain. Often triggered by everyday environmental sounds, auditory hypersensitivity can provoke self-regulatory fear responses such as crying and isolation from sounds. This paper presents SoundFields, an interactive virtual reality game designed to address this area by integrating exposure based therapy techniques into game mechanics and delivering target auditory stimuli to the player rendered via binaural based spatial audio. A pilot study was conducted with six participants diagnosed with ASD who displayed hypersensitivity to specific sounds to evaluate the use of SoundFields as a tool to reduce levels of anxiety associated with identified problematic sounds. During the course of the investigation participants played the game weekly over four weeks and all participants actively engaged with the virtual reality (VR) environment and enjoyed playing the game. Following this period, a comparison of pre- and post-study measurements showed a significant decrease in anxiety linked to target auditory stimuli. The study results therefore suggest that SoundFields could be an effective tool for helping individuals with autism manage auditory hypersensitivity

Investigating alterations of social interaction in psychiatric disorders with dual interactive eye tracking and virtual faces

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Peer reviewedPublisher PD

A Virtual Conversational Agent for Teens with Autism: Experimental Results and Design Lessons

We present the design of an online social skills development interface for teenagers with autism spectrum disorder (ASD). The interface is intended to enable private conversation practice anywhere, anytime using a web-browser. Users converse informally with a virtual agent, receiving feedback on nonverbal cues in real-time, and summary feedback. The prototype was developed in consultation with an expert UX designer, two psychologists, and a pediatrician. Using the data from 47 individuals, feedback and dialogue generation were automated using a hidden Markov model and a schema-driven dialogue manager capable of handling multi-topic conversations. We conducted a study with nine high-functioning ASD teenagers. Through a thematic analysis of post-experiment interviews, identified several key design considerations, notably: 1) Users should be fully briefed at the outset about the purpose and limitations of the system, to avoid unrealistic expectations. 2) An interface should incorporate positive acknowledgment of behavior change. 3) Realistic appearance of a virtual agent and responsiveness are important in engaging users. 4) Conversation personalization, for instance in prompting laconic users for more input and reciprocal questions, would help the teenagers engage for longer terms and increase the system's utility

Knowing me, knowing you: perspectives on awareness in autism

Purpose: This paper raises important questions from the different perspectives on autism research that arose from a seminar on autism and technology, held as part of an ESRC-funded series on innovative technologies for autism. Design/methodology/approach: The paper focuses on the roles of technology in understanding questions about different perspectives on autism: how do people on the spectrum see neurotypicals (people without autism) and vice versa?; how do we use eye-gaze differently from each other?; how might technology influence what is looked at and how we measure this?; what differences might there be in how people use imitation of others?; and finally, how should we study and treat any differences? Findings: We synthesise common themes from invited talks and responses. The audience discussions highlighted the ways in which we take account of human variation, how we can understand the perspective of another, particularly across third-person and second-person approaches in research, and how researchers and stakeholders engage with each other. Originality/value: We argue that the question of perspectives is important for considering how people with autism and neurotypical people interact in everyday contexts, and how researchers frame their research questions and methods. We propose that stakeholders and researchers can fruitfully engage directly in discussions of research, in ways that benefit both research and practice

A Survey: Approaches for Detecting the Autism Spectrum Disorder

A brain disease mean autism spectrum disorder affects a person's ability to connect, communicate, and remember. Though autism is capable of being diagnosed regardless of age, most of the disorder's signs begin to appear around its initial two years of life and increase as time goes on. People with autism suffer from a wide range of difficulties, such sensory problems, action impairments, intellectual disabilities, and psychological disorders including depression and anxiety. Autism has been rising at an unacceptably rapid pace surrounding around the globe. Autism detection involves an enormous amount of time and money. The early detection of autism might be highly advantageous in regards to treating patients with the right medical treatments at the correct moment in time. It could prevent the individual's illnesses before developing severe and could help in decreasing future expenses associated to a diagnosis that was delayed. Thereby, the requirement to develop a rapid, trustworthy, and simple examination device that can make predictions is essential. Autism Spectrum Disorder (ASD) has been gaining momentum presently more quickly than at any time earlier. Diagnostic evaluation of autistic characteristics is extremely expensive and time-consuming as well. The advancement of algorithms for machine learning (ML) and Artificial intelligence (AI) have made it achievable to identify autism fairly earlier. Although the reality of numerous studies have been carried out performed utilising different techniques, these studies have not contributed to any definitive conclusions regarding the capacity of predicting autism attributes in regards to different age categories. Thereby, the objective of this research is to predict Autism among people of all ages and to provide an effective model for prediction using various ML approaches

Using affective avatars and rich multimedia content for education of children with autism

Autism is a communication disorder that mandates early and continuous educational interventions on various levels like the everyday social, communication and reasoning skills. Computer-aided education has recently been considered as a likely intervention method for such cases, and therefore different systems have been proposed and developed worldwide. In more recent years, affective computing applications for the aforementioned interventions have also been proposed to shed light on this problem. In this paper, we examine the technological and educational needs of affective interventions for autistic persons. Enabling affective technologies are visited and a number of possible exploitation scenarios are illustrated. Emphasis is placed in covering the continuous and long term needs of autistic persons by unobtrusive and ubiquitous technologies with the engagement of an affective speaking avatar. A personalised prototype system facilitating these scenarios is described. In addition the feedback from educators for autistic persons is provided for the system in terms of its usefulness, efficiency and the envisaged reaction of the autistic persons, collected by means of an anonymous questionnaire. Results illustrate the clear potential of this effort in facilitating a very promising autism intervention

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