Vocational Rehabilitation of Individuals with Autism Spectrum Disorder with Virtual Reality

2017 ◽  
Vol 10 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Lal Bozgeyikli ◽  
Evren Bozgeyikli ◽  
Andrew Raij ◽  
Redwan Alqasemi ◽  
Srinivas Katkoori ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Vocational Rehabilitation ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Behaviours that Support Self-Understanding for People with Autism Spectrum Disorder in Vocational Rehabilitation

2021 ◽  
Author(s):  
Kazuaki Maebara ◽  
Jun Yaeda
Keyword(s):  
Autism Spectrum Disorder ◽  
Vocational Rehabilitation ◽  
Quantitative Research ◽  
Research Study ◽  
Autism Spectrum ◽  
Current Situation ◽  
Spectrum Disorder ◽  
Qualitative Research Study ◽  
Support Programme ◽  
Environmental Setting

<p>This study identifies behaviours that support self-understanding for people with autism spectrum disorder (ASD) participating in vocational rehabilitation. The qualitative research (Study 1) used conceptual analysis to identify vocational rehabilitation practitioners’ concept of support for self-understanding. The quantitative research (Study 2) surveyed 155 Japanese vocational rehabilitation practitioners using a questionnaire based on the results of Study 1. Exploratory factor analysis of the survey data determined the structure of behaviours that support self-understanding for people with ASD and found three behaviour types: ‘Environmental setting of the current situation’, ‘Promoting awareness of the current situation’, and ‘Reflection based on collected information’. A practitioner was deemed to promote self-understanding support by using a combination of these three behaviours while heeding to the disabling characteristics of people with ASD. Identified support behaviours could be used as a fundamental perspective to develop a support programme to promote self-understanding for people with ASD.</p>

Virtual Reality (VR) for School Children With Autism Spectrum Disorder (ASD)

2019 ◽  
pp. 141-158 ◽  
Author(s):  
Giuliana Guazzaroni ◽  
Anitha S. Pillai
Keyword(s):  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
School Children ◽  
Teaching And Learning ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Self Awareness ◽  
The World ◽  
Training And Support

Various medical and technological organizations are working on the development of unconventional solutions such as therapy and assistance for people with autism spectrum disorder (ASD). Different organizations, researchers and educators have been involved in the study and realization of virtual reality (VR) solutions to be used as therapy, training, and support for these individuals. Previous researches and experiments showed that it is possible to ameliorate the level of concentration, coordination, socialization, communication, self-awareness, and memory in school children treated with these tools. VR environments may offer a total physical involvement of the ASDs that may see the world through virtual immersion and active practice. This chapter presents a way of rethinking teaching and learning.

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.

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