Publication Details




Familiar video stories as a means for children with autism: An analytics approach.


Chung-Hao Chen, Jonna Bobzien, Michail Giannakos, Ann Bruhn, Alexis Brueggeman, Shahram Mohrehkesh, Ming Zhang, Wei-Wen Hsu and Nikos Chrisochoides.


Published in IEEE International Conference on Healthcare Informatics, 10.1109/ICHI.2015.52, October, 2015




Children with Autism Spectrum Disorder (ASD) may experience extreme anxiety and stress when faced with unfamiliar novel situations. The use of video social stories is one common approach to helping children with ASD cope with these situations. Despite some literature on the positive outcomes associated with the use of video social stories, the overall results on the success of this intervention are inconsistent. One possible explanation for these inconsistencies is that the characters portrayed in the video are often generic and therefore unfamiliar. In order to address this hypothesis, Chen et al. developed a video face replacement technology, which presents a unique and individualized video to the child that includes familiar faces and places. With this technology, it was found that the personalized videos encouraged children with ASD to express more positive emotions during a novel experience. Although the children with ASD demonstrated decreased stress and anxiety after viewing the video with the face replacement technology, it is still difficult to ascertain how much of the desired information was actually gleaned from watching the video social story. In order to address this critical issue, an existing video learning analytic system was used to determine how subjects with ASD perceived video materials during the viewing of an individualized video social story. The preliminary results of this pilot study demonstrated that subjects with ASD were capable of interacting with the video and did so in order to better understand the content of the video social story. These preliminary results indicate the need for further investigation into how the user interface and its enhancements can provide precise information on the perception and cognition of the individuals with ASD.




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