Second Version of Google Glass as a Wearable Socio-Affective Aid: Positive School Desirability, High Usability, and Theoretical Framework in a Sample of Children with Autism

Digital Horizons: Enhancing Autism Support with Augmented Reality

PURPOSE

This paper aims to comprehensively review the application of Augmented Reality interventions in supporting individuals diagnosed with Autism Spectrum Disorder. The main research question guiding this review is: What effects do AR interventions have on various aspects of functioning in individuals with ASD?

METHODS

A systematic review methodology was employed to analyze 49 articles published between 2013 and 2023. These articles were selected based on their relevance to AR interventions for individuals with ASD. The review examines the diverse technological landscape of AR interventions, the various platforms utilized, and the effectiveness of different AR techniques.

RESULTS

Findings reveal the prevalence of smartphones, tablets, smart glasses, and head-mounted displays as primary platforms for AR interventions, with positive outcomes reported across various domains including social interaction skills, communication abilities, and academic performance. Marker-based, superimposition-based, and projection-based AR techniques demonstrate potential in creating personalized and engaging experiences tailored to the unique needs of individuals with ASD.

CONCLUSION

Despite progress in communication and social skills interventions, gaps remain in understanding and addressing attention-related issues and emotion recognition. The review underscores the need for more rigorous study designs and objective evaluation methods to ascertain the efficacy of AR interventions for individuals with ASD. Looking ahead, collaborative efforts between researchers, developers, practitioners, and individuals with ASD are crucial for advancing innovation, addressing limitations, and ensuring the meaningful integration of AR technology into interventions aimed at enhancing the quality of life for individuals on the autism spectrum. Further exploration and utilization of the latest advancements in artificial intelligence and affective computing are warranted to develop solutions that effectively address real-world challenges faced by individuals with autism.

The Effects of Augmented Reality on Social Skills in Children with an Autism Diagnosis: A Preliminary Systematic Review

Today, the global prevalence of autism is high and continues to increase dramatically. Effective support and interventions are therefore warranted. Augmented reality (AR), one of the recent modalities of immersive technology, is gaining traction in autism interventions. However, there is currently a lack of reviews on the use of AR in children with autism, and what is more, to the authors' knowledge, no systematic review to date has been carried out to exclusively examine the effects of AR on basic social skills in children with autism. Therefore, an evidence-based systematic review was conducted to fill the gap and answer the specific research question: Can AR support children with an autism diagnosis towards developing or promoting social skills, such as greetings? The initial search yielded a total of 416 records. After excluding duplicate articles and screening the abstract and full text, 13 studies were included for analysis. A narrative approach was employed to synthesise and evaluate the research findings to substantially explore the effects of AR-based social interventions. The favourable role of AR technology in fostering social skills in children diagnosed with autism was widely recognised in the included studies although multiple methodological limitations were identified in relation to the quality of the included studies. Overall, the promising findings may suggest the effectiveness of AR in improving social skills in children with autism. Nonetheless, this field of research still calls for more high-quality studies relying on rigorous methodologies.

Wearable Orofacial Technology and Orthodontics

Wearable technology to augment traditional approaches are increasingly being added to the arsenals of treatment providers. Wearable technology generally refers to electronic systems, devices, or sensors that are usually worn on or are in close proximity to the human body. Wearables may be stand-alone or integrated into materials that are worn on the body. What sets medical wearables apart from other systems is their ability to collect, store, and relay information regarding an individual's current body status to other devices operating on compatible networks in naturalistic settings. The last decade has witnessed a steady increase in the use of wearables specific to the orofacial region. Applications range from supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and better understanding the jaw's functional and parafunctional activities. Orofacial wearable devices may be unimodal or incorporate multiple sensing modalities. The objective data collected continuously, in real time, in naturalistic settings using these orofacial wearables provide opportunities to formulate accurate and personalized treatment strategies. In the not-too-distant future, it is anticipated that information about an individual's current oral health status may provide patient-centric personalized care to prevent, diagnose, and treat oral diseases, with wearables playing a key role. In this review, we examine the progress achieved, summarize applications of orthodontic relevance and examine the future potential of orofacial wearables.

Extended Reality (XR) and telehealth interventions for children or adolescents with autism spectrum disorder: Systematic review of qualitative and quantitative studies

World Health Organization reported that almost one in 100 children is diagnosed with autism spectrum disorder (ASD) worldwide. Extended Reality (XR) and Telehealth interventions are evident to be effective for ASD treatments. While there is no comprehensive systematic review to summarize and evaluate the evidence for promoting the accessibility of different technology-based treatment regiments. This paper aims to verify the efficacy and validity of XR and Telehealth interventions for children and adolescents with ASD. We reviewed 112 studies from databases of PubMed, Web of Science and Cochrane Library, published in English between January 2010 to April 1st, 2022. We found that after interventions, positive improvements for ASD participants were observed in social interaction, acceptance, and engagement, communication and speech, emotion recognition and control, daily living skill, problem behavior reduction, attention, cost reduction, anxiety symptom reduction, pretend play, contextual processing, match to sample skill, and insomnia control. Our findings provide a solid and positive evidence of XR and Telehealth interventions in improving the treatment outcomes for children and adolescents with ASD. In the future, more research with RCTs and standardized outcome measurements are required to establish the therapeutic efficiency of the two interventions independently or combined.

The Potential of AR Solutions for Behavioral Learning: A Scoping Review

In recent years, educational researchers and practitioners have become increasingly interested in new technologies for teaching and learning, including augmented reality (AR). The literature has already highlighted the benefit of AR in enhancing learners’ outcomes in natural sciences, with a limited number of studies exploring the support of AR in social sciences. Specifically, there have been a number of systematic and scoping reviews in the AR field, but no peer-reviewed review studies on the contribution of AR within interventions aimed at teaching or training behavioral skills have been published to date. In addition, most AR research focuses on technological or development issues. However, limited studies have explored how technology affects social experiences and, in particular, the impact of using AR on social behavior. To address these research gaps, a scoping review was conducted to identify and analyze studies on the use of AR within interventions to teach behavioral skills. These studies were conducted across several intervention settings. In addition to this research question, the review reports an investigation of the literature regarding the impact of AR technology on social behavior. The state of the art of AR solutions designed for interventions in behavioral teaching and learning is presented, with an emphasis on educational and clinical settings. Moreover, some relevant dimensions of the impact of AR on social behavior are discussed in more detail. Limitations of the reviewed AR solutions and implications for future research and development efforts are finally discussed.

Machine Learning for Healthcare Wearable Devices: The Big Picture

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

Mobile Augmented Reality Technologies for Autism Spectrum Disorder Interventions: A Systematic Literature Review

Over the past decade, enhanced computing capabilities and mobile technologies have begotten the upsurge of innovative mobile health (mHealth) solutions, and many research efforts have occurred recently in the area of technology-based interventions (TBI) for autism spectrum disorders (ASD). Mobile augmented reality (MAR) refers to AR systems that use the handheld mobile device medium (mobile phones, tablets or smart glasses). This article reports the results of a systematic review undertaken on the use of MAR for ASD-related skills learning from the year 2010. It aims to provide an insight into the current state of research on MAR interventions and to provide guidance to relevant designers and researchers. We searched seven databases and retrieved 625 articles initially. After exclusion and screening, 36 articles were reviewed reporting on using MAR to improve various skills of children and adolescents with ASD, and 10 research questions related to PICO (P: Population, I: Intervention, C: Comparison, O: Outcomes) were addressed. This study identifies challenges that still exist in the research efforts towards the development of applications exploiting the MAR for ASD interventions: technology issues, research design consideration, subjective assessment etc. The studies examined suggest researchers should focus on users and improve the quality of the MAR app. In addition, more effective research methods and evaluation methods could be involved in future studies to facilitate the development of MAR intervention applications.

Retracted: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

BACKGROUND

In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children.

OBJECTIVE

Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening.

METHODS

After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network-based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning-based classifiers and randomly selected common variants.

RESULTS

The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools.

CONCLUSIONS

Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

Exploring the Impact of Augmented Reality in Children and Adolescents with Autism Spectrum Disorder: A Systematic Review

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by persistent difficulties in communication and social interaction along with a restriction in interests and the presence of repetitive behaviors. The development and use of augmented reality technology for autism has increased in recent years. However, little is known about the impact of these virtual reality technologies on clinical health symptoms. The aim of this systematic review was to investigate the impact of augmented reality through social, cognitive, and behavioral domains in children and adolescents with autism. This study is the first contribution that has carried out an evidence-based systematic review including relevant science databases about the effectiveness of augmented reality-based intervention in ASD. The initial search identified a total of 387 records. After the exclusion of papers that are not research studies and are duplicated articles and after screening the abstract and full text, 20 articles were selected for analysis. The studies examined suggest promising findings about the effectiveness of augmented reality-based treatments for the promotion, support, and protection of health and wellbeing in children and adolescents with autism. Finally, possible directions for future work are discussed.

The use of wearable technology to measure and support abilities, disabilities and functional skills in autistic youth: a scoping review

BACKGROUND

Wearable technology (WT) to measure and support social and non-social functioning in Autism Spectrum Disorder (ASD) has been a growing interest of researchers over the past decade. There is however limited understanding of the WTs currently available for autistic individuals, and how they measure functioning in this population.

OBJECTIVE

This scoping review explored the use of WTs for measuring and supporting abilities, disabilities and functional skills in autistic youth.

METHOD

Four electronic databases were searched to identify literature investigating the use of WT in autistic youth, resulting in a total of 33 studies being reviewed. Descriptive and content analysis was conducted, with studies subsequently mapped to the ASD International Classification of Functioning, Disability and Health Core-sets and the ICF Child and Youth Version (ICF-CY).

RESULTS

Studies were predominately pilot studies for novel devices. WTs measured a range of physiological and behavioural functions to objectively measure stereotypical motor movements, social function, communication, and emotion regulation in autistic youth in the context of a range of environments and activities.

CONCLUSIONS

While this review raises promising prospects for the use of WTs for autistic youth, the current evidence is limited and requires further investigation.

Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses

BACKGROUND

Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings.

OBJECTIVE

This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs).

METHODS

We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision-enabled methods for pupil tracking and world-gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants.

RESULTS

Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects.

CONCLUSIONS

Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype.

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study

BACKGROUND

Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. The incidence of ASD has increased in recent years; it is now estimated that approximately 1 in 40 children in the United States are affected. Due in part to increasing prevalence, access to treatment has become constrained. Hope lies in mobile solutions that provide therapy through artificial intelligence (AI) approaches, including facial and emotion detection AI models developed by mainstream cloud providers, available directly to consumers. However, these solutions may not be sufficiently trained for use in pediatric populations.

OBJECTIVE

Emotion classifiers available off-the-shelf to the general public through Microsoft, Amazon, Google, and Sighthound are well-suited to the pediatric population, and could be used for developing mobile therapies targeting aspects of social communication and interaction, perhaps accelerating innovation in this space. This study aimed to test these classifiers directly with image data from children with parent-reported ASD recruited through crowdsourcing.

METHODS

We used a mobile game called Guess What? that challenges a child to act out a series of prompts displayed on the screen of the smartphone held on the forehead of his or her care provider. The game is intended to be a fun and engaging way for the child and parent to interact socially, for example, the parent attempting to guess what emotion the child is acting out (eg, surprised, scared, or disgusted). During a 90-second game session, as many as 50 prompts are shown while the child acts, and the video records the actions and expressions of the child. Due in part to the fun nature of the game, it is a viable way to remotely engage pediatric populations, including the autism population through crowdsourcing. We recruited 21 children with ASD to play the game and gathered 2602 emotive frames following their game sessions. These data were used to evaluate the accuracy and performance of four state-of-the-art facial emotion classifiers to develop an understanding of the feasibility of these platforms for pediatric research.

RESULTS

All classifiers performed poorly for every evaluated emotion except happy. None of the classifiers correctly labeled over 60.18% (1566/2602) of the evaluated frames. Moreover, none of the classifiers correctly identified more than 11% (6/51) of the angry frames and 14% (10/69) of the disgust frames.

CONCLUSIONS

The findings suggest that commercial emotion classifiers may be insufficiently trained for use in digital approaches to autism treatment and treatment tracking. Secure, privacy-preserving methods to increase labeled training data are needed to boost the models' performance before they can be used in AI-enabled approaches to social therapy of the kind that is common in autism treatments.

Review and Implementation of Self-Help and Automated Tools in Mental Health Care

Self-help and automated technologies can be useful for behavioral and mental health education and interventions. These technologies include interactive media, online courses, artificial intelligence-powered chatbots, voice assistants, and video games. Self-help media can include books, videos, audible media like podcasts, blog and print articles, and self-contained Internet sites. Social media, online courses, and mass-market mobile apps also can include such media. These technologies serve to decrease geospatial, temporal, and financial barriers. This article describes different self-help and automated technologies, how to implement such technologies in existing clinical services, and how to implement according to patient needs.

The Use of Information Communication Technologies Among Children With Autism Spectrum Disorders: Descriptive Qualitative Study

BACKGROUND

The prevalence of Autism Spectrum Disorder (ASD) appears to be increasing globally due to the complex interaction of multiple biopsychosocial and environmental factors. Mobile phones, tablets, and other electronic gadgets have transformed our means of communication, and have also changed both healthcare and how we learn. These technological enhancements may have a positive impact on the lives of children, but there is currently a global scarcity of information on how information technology influences the education of children with ASD.

OBJECTIVE

This study was conducted in Rwandan schools and communities, and aimed to understand the perceptions of students with ASD, their parents, and their teachers, on the use of Information and Communication Technology (ICT) in the education of those with ASD.

METHODS

This qualitative descriptive study was conducted from December 2017 to July 2018. Researchers conducted four focus group discussions (FGDs) with 54 participants from different backgrounds: teachers, parents, and students with ASD. Each of the FGDs took approximately two and a half hours. A predefined set of open-ended questions were selected to discover people's perceptions regarding assistive technologies used in ASD, their effectiveness, the scope of using them in their context, and upcoming challenges during implementation. The interviews were recorded, transcribed, and analyzed.

RESULTS

The findings of the study revealed seven key themes: (1) the use of ICT for the education of children with ASD; (2) existing augmentative facilities for learning; (3) current patterns of use of ICT in education; (4) preferred areas of learning for ASD students; (5) integration of ICT into educational programs; (6) areas of interest outside the classroom; and (7) future opportunities and challenges in Rwanda. We found most of the study participants assumed that appropriate technology and related innovations might solve the challenges faced by learners with ASD in classrooms. Moreover, they thought that children with ASD more so enjoyed watching television, playing digital games, and drawing objects using gadgets than interacting with people or playing with other children.

CONCLUSIONS

The use of various low-cost technical devices can aid with teaching and the education of children with autism in Rwanda. However, this area requires further research to discover the impact ICT can have on the education of children with ASD, so this study may become a starting point for further research in the area.

Augmented Reality in Educational Inclusion. A Systematic Review on the Last Decade

The use of Augmented Reality (AR) to achieve educational inclusion has been not deeply explored. This systematic review describes the current state of using AR as an educational technology that takes into consideration the needs of all students including those with a disability. It is done through the analysis of factors, such as the advantages of AR, its limitations, uses, challenges, its scope in the educational field, the attended population and the positive or negative effects of its use in learning scenarios that involve students with diverse educational needs. A total of 50 studies between 2008 and 2018 were analyzed through searching in three interdisciplinary databases: Scopus, Web of Science, and Springer link. For this, the methodological stages considered were planning the review, search, analysis of literature and results report. After analyzing the results, it was possible to demonstrate that the use of AR for inclusive education in the field of sciences is where more studies have been conducted. In regard to the population with disabilities, among the most representative advantages reported were the motivation, interaction and generating interest on the part of the student. At the same time, an important methodological limitation identified was the size of the sample; some investigations were done with two or three subjects, some studies Single Subject Designs were found. In terms of the population attended, the studies generally included students with different impairments (hearing, visual, motor or cognitive), minorities (ethnic, vulnerable), leaving aside other groups excluded as exceptional talents and immigrants, which could be explored in the future. Despite different problems to be addressed, few frameworks to the diversity attention in education were reported, and there was no model and methodology in inclusive education considered in the studies. Finally, from this review we have identified open issues that could give rise to new research in the subject of using AR to favor the creation of inclusive learning scenarios.

Digital Attention-Related Augmented-Reality Game: Significant Correlation between Student Game Performance and Validated Clinical Measures of Attention-Deficit/Hyperactivity Disorder (ADHD)

As many as half of school children with autism spectrum disorder (ASD) exhibit symptoms of attention-deficit/hyperactivity disorder (ADHD), resulting in marked negative academic, social, and behavioral outcomes. The focus of the US Food and Drug Administration (FDA) on real-world data from novel digital sources, and the emergence of Current Procedural Terminology (CPT) codes to reimburse for digital monitoring and neurobehavioral testing suggest an increasing acceptance of the role of technology in augmenting clinical care and research. Empowered Brain is an augmented reality and artificial intelligence-based social-emotional communication aid for students with ASD. In this study, student performance on Empowered Brain is correlated to validated clinical measures of ADHD. Seven high school students with a diagnosis of ASD were recruited from a public high school. All students were assessed for severity of ADHD-related symptoms via three clinical gold-standard assessments, namely the Aberrant Behavioral Checklist (ABC), Social Responsiveness Scale 2 (SRS-2), and Teacher Report Form (TRF). Students used Empowered Brain over a one-week period. We measured the correlation of student in-game performance (as measured by point- and star-based rewards) relative to the hyperactivity subscale of the ABC (ABC-H), and the ADHD-subscale of the TRF. All seven students completed the study and managed to successfully use Empowered Brain. Students received a culminative total of 32 sessions, an average of 4.6 sessions per student (range 2–8). Student in-game performance demonstrated highly significant correlation relative to ABC-H (points: p = 0.0013; stars: p = 0.0013), and significant correlation to TRF ADHD scores (points: p = 0.012; stars: p = 0.012). No adverse effects were noted among students who used Empowered Brain. New technologies may herald novel ways of identifying and characterizing symptoms of ADHD in student populations. This study provides evidence that Empowered Brain in-game performance correlates with ADHD symptom severity in students with ASD. Larger samples are required to validate these findings, with more diverse participants that can also widen the generalizability of these findings to a broader range of brain conditions that manifest with inattention, impulsivity, and hyperactivity. Through further research, we may find that such technologies can help us to identify and longitudinally monitor such symptoms, and potentially aid in severity stratification and digital phenotyping.

Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks

BACKGROUND

Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis.

OBJECTIVE

We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips.

METHODS

Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as "Autism" or "Not autism," (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ≥8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study.

RESULTS

For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ≥8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity (ρ=-0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=-0.19, P=.04), number of family members with autism and sensitivity (r=-0.195, P=.04), and number of family members with autism and precision (r=-0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001).

CONCLUSIONS

Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations.

Assessment of Google Glass for Photographic Documentation in Veterinary Forensic Pathology: Usability Study

Background

Google Glass is a head-mounted device designed in the shape of a pair of eyeglasses equipped with a 5.0-megapixel integrated camera and capable of taking pictures with simple voice commands.

Objective

The objective of our study was to determine whether Google Glass is fit for veterinary forensic pathology purposes.

Methods

A total of 44 forensic necropsies of 2 different species (22 dogs and 22 cats) were performed by 2 pathologists; each pathologist conducted 11 necropsies of each species and, for each photographic acquisition, the images were taken with a Google Glass device and a Nikon D3200 digital single-lens reflex (DSLR) camera. The pictures were collected, divided into 3 groups (based on the external appearance of the animal, organs, and anatomical details), and evaluated by 5 forensic pathologists using a 5-point score system. The parameters assessed were overall color settings, region of interest, sharpness, and brightness. To evaluate the difference in mean duration between necropsies conduced with Google Glass and DSLR camera and to assess the battery consumption of the devices, an additional number of 16 necropsies were performed by the 2 pathologists. In these cases, Google Glass was used for photographic reports in 8 cases (4 dogs and 4 cats) and a Nikon D3200 reflex camera in the other 8 cases. Statistical evaluations were performed to assess the differences in ratings between the quality of the images taken with both devices.

Results

The images taken with Google Glass received significantly lower ratings than those acquired with reflex camera for all 4 assessed parameters (P<.001). In particular, for the pictures of Groups A and B taken with Google Glass, the sum of frequency of ratings 5 (very good) and 4 (good) was between 50% and 77% for all 4 assessed parameters. The lowest ratings were observed for the pictures of Group C, with a sum of frequency of ratings 5 and 4 of 21.1% (342/1602) for region of interest, 26% (421/1602) for sharpness, 35.5% (575/1602) for overall color settings, and 61.4% (995/1602) for brightness. Furthermore, we found a significant reduction in the mean execution time for necropsy conduced with the Google Glass with respect to the reflex group (P<.001). However, Google Glass drained the battery very quickly.

Conclusions

These findings suggest that Google Glass is usable in veterinary forensic pathology. In particular, the image quality of Groups A and B seemed adequate for forensic photographic documentation purposes, although the quality was lower than that with the reflex camera. However, in this step of development, the high frequency of poor ratings observed for the pictures of Group C suggest that the device is not suitable for taking pictures of small anatomical details or close-ups of the injuries.

Improved Socio-Emotional and Behavioral Functioning in Students with Autism Following School-Based Smartglasses Intervention: Multi-Stage Feasibility and Controlled Efficacy Study

Background: Students with Autism Spectrum Disorder (ASD) commonly demonstrate prominent social communication deficits, symptoms of attention-deficit/hyperactivity disorder, and chronic irritability. These challenges hinder academic progress and frequently persist despite educational, behavioral, and medical interventions. An assistive smartglasses technology may aid these individuals, especially if the technology is efficacious in ecologically-valid school settings. This study explored the feasibility and efficacy of Empowered Brain, a computerized smartglasses intervention designed as a socio-emotional behavioral aid for students with ASD. Methods: This two-part six-week study involved four school children with ASD from a public elementary school. The study incorporated an initial three-week feasibility stage followed by a three-week controlled longitudinal efficacy stage. Both stages involved the use of a twice-daily socio-emotional intervention with the smartglasses. Educators completed pre-intervention and post-intervention Aberrant Behavioral Checklist (ABC) ratings at the start of the feasibility stage, and weekly during the efficacy stage. Primary outcome measures were improvements in the ABC subscales of irritability, hyperactivity, and social withdrawal. Results: Students in both feasibility and efficacy stages demonstrated improvements (decreases) in irritability, hyperactivity, and social withdrawal compared to a baseline period and control periods, respectively. Participants in the controlled efficacy stage demonstrated decreased ABC subscale scores of 90% for irritability, 41.6% for hyperactivity, and 45.6% for social withdrawal. An intervention exposure-response improvement in irritability and hyperactivity was found during the efficacy stage. Educators rated the technology as superior or vastly superior compared to other assistive technologies. Conclusion: A substantial number of school children with ASD demonstrate chronic and impairing cognitive and behavioral challenges. This study provides evidence that Empowered Brain, a smartglasses-based socio-emotional aid for autism, is both feasible and efficacious in improving symptoms of social withdrawal, irritability, and hyperactivity in students with autism. The improvement is demonstrated as part of a longitudinal school-based intervention. Further studies involving larger samples and incorporation of randomized controlled trial methodology are underway to further elucidate the impact of this technology.

Safety and Lack of Negative Effects of Wearable Augmented-Reality Social Communication Aid for Children and Adults with Autism

There is a growing interest in the use of augmented reality (AR) to assist children and adults with autism spectrum disorders (ASD); however, little investigation has been conducted into the safety of AR devices, such as smartglasses. The objective of this report was to assess the safety and potential negative effects of the Empowered Brain system, a novel AR smartglasses-based social communication aid for people with ASD. The version of the Empowered Brain in this report utilized Google Glass (Google, Mountain View, CA, USA) as its hardware platform. A sequential series of 18 children and adults, aged 4.4 to 21.5 years (mean 12.2 years), with clinically diagnosed ASD of varying severity used the system. Users and caregivers were interviewed about the perceived negative effects and design concerns. Most users were able to wear and use the Empowered Brain (n = 16/18, 89%), with most of them reporting no negative effects (n = 14/16, 87.5%). Caregivers observed no negative effects in users (n = 16/16, 100%). Most users (77.8%) and caregivers (88.9%) had no design concerns. This report found no major negative effects in using an AR smartglasses-based social communication aid across a wide age and severity range of people with ASD. Further research is needed to explore longer-term effects of using AR smartglasses in this population.

Longitudinal Socio-Emotional Learning Intervention for Autism via Smartglasses: Qualitative School Teacher Descriptions of Practicality, Usability, and Efficacy in General and Special Education Classroom Settings

There is considerable demand for special education services for the over half a million students with autism in the United States. While assistive technology may augment educational services, its implementation is often prevented by a number of practical and attitudinal barriers. These barriers are especially pertinent for the newest and thus least familiar digital systems, such as computerized smartglasses loaded with specialized software modules. Computerized smartglasses are a technology that has already been shown to have an ability to deliver educational interventions through augmented reality. With this in mind, we sought to understand how school educators received and assessed the practicality of a smartglasses-based educational intervention in a single-subject study. The intervention was designed to aid with attention and social educational learning in autism. The intervention was delivered twice a day during a two-week study on a 13-year-old student with autism who was attending a mainstream middle school in Massachusetts. Three different school educators delivered the intervention: the student’s general education teacher, special education teacher, and paraprofessional. Educators recorded their attitudes, the practicality of the technology, and its impact on the student and their classroom through the use of a digital log and a series of in-person interviews. Overall, the school educators experienced a positive view of the smartglasses. The smartglasses intervention was found to be logistically practical to implement, easily usable by both the educator and student, and not time-consuming to learn or implement. Educators also identified the experience as being fun for the student, and felt that the student demonstrated improvement in his verbal and non-verbal skills. There were no adverse effects on the other students or the classroom, and the technology did not result in a distraction. These findings suggest that social skills interventions delivered by smartglasses may be practical, useful, and may lead to improvements in social communication skills. Further research on smartglasses may help to clarify the future role for augmenting special education in students with autism.

Improvement of Attention-Deficit/Hyperactivity Disorder Symptoms in School-Aged Children, Adolescents, and Young Adults With Autism via a Digital Smartglasses-Based Socioemotional Coaching Aid: Short-Term, Uncontrolled Pilot Study

BACKGROUND

People with autism spectrum disorder (ASD) commonly experience symptoms related to attention-deficit/hyperactivity disorder (ADHD), including hyperactivity, inattention, and impulsivity. One-third of ASD cases may be complicated by the presence of ADHD. Individuals with dual diagnoses face greater barriers to accessing treatment for ADHD and respond less positively to primary pharmacologic interventions. Nonpharmacologic technology-aided tools for hyperactivity and inattention in people with ASD are being developed, although research into their efficacy and safety remains limited.

OBJECTIVE

The objective of this preliminary study was to describe the changes in ADHD-related symptoms in children, adolescents, and young adults with ASD immediately after use of the Empowered Brain system, a behavioral and social communication aid for ASD running on augmented reality smartglasses.

METHODS

We recruited 8 children, adolescents, and young adults with ASD (male to female ratio of 7:1, mean age 15 years, range 11.7-20.5 years) through a Web-based research signup form. The baseline score on the hyperactivity subscale of the Aberrant Behavioral Checklist (ABC-H), a measure of hyperactivity, inattention, and impulsivity, determined their classification into a high ADHD-related symptom group (n=4, ABC-H≥13) and a low ADHD-related symptom group (n=4, ABC-H<13). All participants received an intervention with Empowered Brain, where they used smartglasses-based social communication and behavioral modules while interacting with their caregiver. We then calculated caregiver-reported ABC-H scores at 24 and 48 hours after the session.

RESULTS

All 8 participants were able to complete the intervention session. Postintervention ABC-H scores were lower for most participants at 24 hours (n=6, 75%) and for all participants at 48 hours (n=8, 100%). At 24 hours after the session, average participant ABC-H scores decreased by 54.9% in the high ADHD symptom group and by 20% in the low ADHD symptom group. At 48 hours after the session, ABC-H scores compared with baseline decreased by 56.4% in the high ADHD symptom group and by 66.3% in the low ADHD symptom group.

CONCLUSIONS

This study provides initial evidence for the possible potential of the Empowered Brain system to reduce ADHD-related symptoms, such as hyperactivity, inattention, and impulsivity, in school-aged children, adolescents, and young adults with ASD. This digital smartglasses intervention can potentially be targeted at a broader array of mental health conditions that exhibit transdiagnostic attentional and social communication deficits, including schizophrenia and bipolar disorder. Further research is required to understand the clinical importance of these observed changes and to conduct longitudinal studies on this intervention with control groups and larger sample sizes.