An Immersive Computer-Mediated Caregiver-Child Interaction System for Young Children With Autism Spectrum Disorder

Bibliometric analysis of research themes and trends in childhood autism spectrum disorders from 2012 to 2021

Background

Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental conditions that affect people worldwide. Early diagnosis and clinical support help achieve good outcomes. However, medical system structure and restricted resource availability create challenges that increase the risk of poor outcomes. Understanding the research progress of childhood ASD in recent years, based on clinical literature reports, can give relevant researchers and rehabilitation therapists more resonable research guides.

Objective

This bibliometric study aimed to summarize themes and trends in research on childhood ASD and to suggest directions for future enquiry.

Methods

Citations were downloaded from the Web of Science Core Collection database on childhood ASD published from 1 January 2012, to 31 December 2021. The retrieved information was analyzed using CiteSpace.5.8. R3, and VOS viewer.

Results

A total of 7,611 papers were published across 103 areas. The United States was the leading source of publications. The clusters that have continued into 2020 include coronavirus disease 2019, gut microbiota, and physical activity, which represent key research topics. Keywords with frequency spikes during 2018-2021 were "disabilities monitoring network," "United States," and "caregiver."

Conclusions

The Autism and Developmental Disabilities Monitoring Network in the United States can be used as a reference for relevant workers worldwide. An intelligent medical assistant system is being developed. Further studies are required to elucidate challenges associated with caring for a child with ASD.

Measuring Social Modulation of Gaze in Autism Spectrum Condition With Virtual Reality Interviews

Gaze behavior in dyadic conversations can indicate active listening and attention. However, gaze behavior that is different from the engagement expected during neurotypical social interaction cues may be interpreted as uninterested or inattentive, which can be problematic in both personal and professional situations. Neurodivergent individuals, such as those with autism spectrum conditions, often exhibit social communication differences broadly including via gaze behavior. This project aims to support situational social gaze practice through a virtual reality (VR) mock job interview practice using the HTC Vive Pro Eye VR headset. We show how gaze behavior varies in the mock job interview between neurodivergent and neurotypical participants. We also investigate the social modulation of gaze behavior based on conversational role (speaking and listening). Our three main contributions are: (i) a system for fully-automatic analysis of social modulation of gaze behavior using a portable VR headset with a novel realistic mock job interview, (ii) a signal processing pipeline, which employs Kalman filtering and spatial-temporal density-based clustering techniques, that can improve the accuracy of the headset's built-in eye-tracker, and (iii) being the first to investigate social modulation of gaze behavior among neurotypical/divergent individuals in the realm of immersive VR.

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

Autism spectrum is a brain development condition that impairs an individual's capacity to communicate socially and manifests through strict routines and obsessive-compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81-84%, with a normalized discounted cumulative gain of 79-81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models' treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.

Enhancing Joint Attention Skills in Children on the Autism Spectrum through an Augmented Reality Technology-Mediated Intervention

In the present study, the effects of an intervention based on an augmented reality technology called Pictogram Room were examined. The objective of the intervention was to improve the responding to joint attention (RJA) skills of gaze following and pointing in six children on the autism spectrum between 3 and 8 years old. A multiple baseline single-subject experimental design was conducted for 12 weeks in a school setting. Results indicated that all of the participant children improved performance in RJA following the intervention. Improvements were maintained over time and generalised to real-world situations. These findings demonstrate that autistic children can improve their RJA skills with a targeted and engaging intervention based on an accessible augmented reality technology tool.