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

[EN] 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.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). 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SUNY Geneseo’s Seventeenth Annual GREAT Day. Geneseo Recognizing Excellence, Achievement & Talent Day is a college-wide symposium celebrating the creative and scholarly endeavors of our students. http://www.geneseo.edu/great_dayhttps://knightscholar.geneseo.edu/program-2007/1017/thumbnail.jp

A QUALITATIVE STUDY OF THE PERCEIVED HEALTH BENEFITS OF A THERAPEUTIC RIDING PROGRAM FOR CHILDREN WITH AUTISM SPECTRUM DISORDERS

Therapeutic horseback riding can be recommended as a useful health promotion intervention for individuals with disabilities who face challenges to optimal health and wellness. This qualitative study examined the perceived benefits of a therapeutic riding program for children with autism spectrum disorders (ASD), with particular focus on aspects that can potentially help maximize the physical, emotional, and social health of this population. This study utilized multiple methods to gain an in-depth perspective on the benefits of a therapeutic riding program based at Central Kentucky Riding for Hope in Lexington, Kentucky, for subjects presenting primarily with ASD. Focus groups were held with five instructors and five class volunteers, and semi-structured personal interviews were conducted with two staff members and the parents and family members of 15 children diagnosed with ASD who were currently enrolled a riding session. Client records containing medical history, lesson plans and client evaluations were also reviewed. Thematic analysis of the data supported perceived gains in the areas of physical, cognitive, psychological, and social development and also highlighted additional support mechanisms for family members of the clients. Some of the most common benefits reported included increased physicality, improved focus and attention, modification of inappropriate behaviors, enhanced self-concept, and increased social interaction and communication. Major factors believed to affect the success of this intervention were the unique movement and sensory stimulation of the horse, the supportive environment of the facility, and the increased motivation for the children to participate and complete the structured activities and exercises required in the riding class setting. Results of this study encourage the utilization of therapeutic riding as an effective health promotion intervention for individuals with ASD. Recommendations for future research efforts include analysis of the effects of deep sensory pressure and the movement provided by the horse on the emotional regulation and cognitive processing of children with ASD. Study designs isolating the variable of the horse’s presence could further clarify the nature of the animal’s role in similar interventions. Quantitative studies with larger samples measuring specific cognitive, psychological, and social variables not previously studied but revealed in this data are also encouraged