Conversation During a Virtual Reality Task Reveals New Structural Language Profiles of Children with ASD, ADHD, and Comorbid Symptoms of Both

2021 ◽  
Author(s):  
Cynthia Boo ◽  
Nora Alpers-Leon ◽  
Nancy McIntyre ◽  
Peter Mundy ◽  
Letitia Naigles
Keyword(s):  
Virtual Reality ◽  
Children With Asd ◽  
Language Profiles ◽  
Structural Language

scholarly journals Machine Learning and Virtual Reality on Body Movements’ Behaviors to Classify Children with Autism Spectrum Disorder

2020 ◽  
Vol 9 (5) ◽  
pp. 1260 ◽  
Author(s):  
Mariano Alcañiz Raya ◽  
Javier Marín-Morales ◽  
Maria Eleonora Minissi ◽  
Gonzalo Teruel Garcia ◽  
Luis Abad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Quantitative Methods ◽  
Real Life ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Body Movements ◽  
Children With Asd

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements’ frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients’ subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements’ biomarkers that could contribute to improving ASD diagnosis.

scholarly journals Reaction and Learning Evaluation of a Non-immersive Virtual Reality Application for Children with Autism Spectrum Disorder

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1020
Author(s):  
Muhamad Irfan Rosli ◽  
Zarina Embi ◽  
Junaidi Abdullah ◽  
Mohd Ali Samsudin ◽  
Mohamad Izani Zainal Abidin ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Positive Trend ◽  
Immersive Virtual Reality ◽  
Children With Asd ◽  
Post Test ◽  
Level 1 ◽  
Level 2

Background: Autism Spectrum Disorder (ASD) is a complex developmental condition that involves persistent challenges in social interaction, speech, and nonverbal communication, in addition to repetitive or restrictive behaviours. For decades, children with ASD have been familiarising themselves with information and communication technologies (ICT) in their training and diagnosis. One of the ICT areas, namely non-immersive virtual reality (NIVR), has become a noticeable tool to help ASD children in their social training. It provides extensive virtual interaction, a safe environment, and is affordable. An NIVR application is developed to assist the intervention on ASD children. However, the whole experiences of the training need to be validated to conclude its effectiveness.   Methods: A case study was employed as the research method. An evaluation of NIVR application using multiple sources of evidence was guided by Kirkpatrick Model of Evaluation (KME) which was executed via questionnaires, pre- and post-test. The main objectives of this research were to evaluate level 1 and 2 of KME. The target for Level 1 is to assess the reactions to the NIVR application. Level 2 is to gauge the knowledge, confidence, or mindset of participants. Level 2 covers the evaluation prior to the training (pre-test) and after the training (post-test).   Results: On average the ASD children had good experiences and were able to improve their social skills with the NIVR application. Therefore, the combination of serious game, analytics and specific VR type provides good data assessment, facilitate comfortable training, and can be an effective intervention for children with ASD.  Conclusion: The positive trend on both levels shows that the application has a good potential to be used in ASD training. The results could be improved in a higher number of participants. Currently, only a limited number of research participants could be obtained due to the COVID-19 pandemic.

A Serious Game as an Auxiliary Tool for the Learning Process of Children With ASD

2019 ◽  
pp. 524-553
Author(s):  
Salatiel Dantas Silva ◽  
Francisco Milton Mendes Neto ◽  
Rodrigo Monteiro De Lima ◽  
Patrício de Alencar Silva ◽  
Karla Rosane Do Amaral Demoly ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Learning Process ◽  
Real World ◽  
Learning Objects ◽  
Serious Game ◽  
Spectrum Disorder ◽  
Learning Object ◽  
Interpersonal Interaction ◽  
Children With Asd

This chapter presents the game K-Hunters, a serious game with the purposes of decreasing the isolation time and helping the learning process of children with autistic spectrum disorder (ASD). The game uses geolocation, virtual reality, and augmented reality techniques to provide an environment to hunt and capture virtual monsters, holders of knowledge. These monsters are geographically spread throughout the real world and can be associated to learning objects. Through mobile devices, the users can go out hunting the monsters, capture them, and view their learning object-related content. Thus, the users are encouraged to get out of their isolation, to search for the virtual monsters, to obtain knowledge, as well as being inserted in scenarios favorable to interpersonal interaction.

Virtual Reality–Based Social Skills Training for Children With Autism Spectrum Disorder

2020 ◽  
pp. 016264342094560 ◽  
Author(s):  
Fengfeng Ke ◽  
Jewoong Moon ◽  
Zlatko Sokolikj
Keyword(s):  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Social Skills ◽  
Learning Environment ◽  
Children With Autism ◽  
Skills Training ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Children With Asd ◽  
Skills Learning

In this study, the researchers explored the usage of a virtual reality (VR)–based social skills learning environment for children with autism spectrum disorder (ASD). Using OpenSimulator, the researchers constructed a desktop VR-based learning environment that supports social-oriented role-play, gaming, and design by children with ASD. Seven 10–14 years old children with ASD participated in this VR-based social skills program for 20+ hr on average. Data were collected via screen recording and observation of play- and design-oriented social skills enactment and pre- and postintervention Social Communication and Skills Questionnaires. Participants demonstrated an increased level of successful social skills performance from the baseline to the intervention phase. The findings provided preliminary evidence for the usage of a VR-based social skills learning environment for children with ASD.

scholarly journals A Distinction Between Linguistic and Social Pragmatics Helps the Precise Characterization of Pragmatic Challenges in Children With Autism Spectrum Disorders and Developmental Language Disorder

2020 ◽  
Vol 63 (5) ◽  
pp. 1494-1508 ◽  
Author(s):  
Clara Andrés-Roqueta ◽  
Napoleon Katsos
Keyword(s):  
Autism Spectrum Disorders ◽  
Language Disorder ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Developmental Language Disorder ◽  
Linguistic Pragmatics ◽  
Children With Asd ◽  
Spectrum Disorders ◽  
And Performance ◽  
Structural Language

Purpose Children with autism spectrum disorders (ASDs) and children with developmental language disorder (DLD) face challenges with pragmatics, but the nature and sources of these difficulties are not fully understood yet. The purpose of this study was to compare the competence of children with ASD and children with DLD in two pragmatics tasks that place different demands on theory of mind (ToM) and structural language. Method Twenty Spanish-speaking children with ASD, 20 with DLD, and 40 age- and language-matched children with neurotypical development were assessed using two pragmatics tasks: a linguistic pragmatics task, which requires competence with structural language, and a social pragmatics task, which requires competence with ToM as well. Results For linguistic pragmatics, the ASD group performed similarly to the DLD and language-matched groups, and performance was predicted by structural language. For social pragmatics, the ASD group performed lower than the DLD and language-matched groups, and performance was predicted both by structural language and ToM. Conclusions Children with ASD and children with DLD face difficulties in linguistic pragmatics tasks, in keeping with their structural language. Children with ASD face exceptional difficulties with social pragmatics tasks, due to their difficulties with ToM. The distinction between linguistic and social pragmatic competences can inform assessment and intervention for pragmatic difficulties in different populations.

The Influence of Language Context on Repetitive Speech Use in Children With Autism Spectrum Disorder

2020 ◽  
Vol 29 (1) ◽  
pp. 327-334 ◽  
Author(s):  
Allison Gladfelter ◽  
Cassidy VanZuiden
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Individual Characteristics ◽  
Receptive Vocabulary ◽  
Spectrum Disorder ◽  
School Age Children ◽  
Social Responsiveness ◽  
Contributing Factors ◽  
Children With Asd ◽  
Language Context

Purpose Although repetitive speech is a hallmark characteristic of autism spectrum disorder (ASD), the contributing factors that influence repetitive speech use remain unknown. The purpose of this exploratory study was to determine if the language context impacts the amount and type of repetitive speech produced by children with ASD. Method As part of a broader word-learning study, 11 school-age children with ASD participated in two different language contexts: storytelling and play. Previously collected language samples were transcribed and coded for four types of repetitive speech: immediate echolalia, delayed echolalia, verbal stereotypy, and vocal stereotypy. The rates and proportions of repetitive speech were compared across the two language contexts using Wilcoxon signed-ranks tests. Individual characteristics were further explored using Spearman correlations. Results The children produced lower rates of repetitive speech during the storytelling context than the play-based context. Only immediate echolalia differed between the two contexts based on rate and approached significance based on proportion, with more immediate echolalia produced in the play-based context than in the storytelling context. There were no significant correlations between repetitive speech and measures of social responsiveness, expressive or receptive vocabulary, or nonverbal intelligence. Conclusions The children with ASD produced less immediate echolalia in the storytelling context than in the play-based context. Immediate echolalia use was not related to social skills, vocabulary, or nonverbal IQ scores. These findings offer valuable insights into better understanding repetitive speech use in children with ASD.

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