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Kalantarian, Haik; Jedoui, Khaled; Washington, Peter; Tariq, Qandeel; Dunlap, Kaiti; Schwartz, Jessey; Wall, Dennis P.
Artificial intelligence in medicine, July 2019, 2019-07-00, 20190701, Volume: 98Journal Article
•Autism spectrum disorder (ASD) affects 750,000 American Children under the age of 10.•Emotion classifiers integrated into mobile solutions can be used for screening and therapy.•Emotion classifiers do not generalize well to children due to a lack of labeled training data.•We propose a method of aggregating emotive video through a mobile game.•We demonstrate that several algorithms can automatically label frames from video derived from the game. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive behaviors, narrow interests, and deficits in social interaction and communication ability. An increasing emphasis is being placed on the development of innovative digital and mobile systems for their potential in therapeutic applications outside of clinical environments. Due to recent advances in the field of computer vision, various emotion classifiers have been developed, which have potential to play a significant role in mobile screening and therapy for developmental delays that impair emotion recognition and expression. However, these classifiers are trained on datasets of predominantly neurotypical adults and can sometimes fail to generalize to children with autism. The need to improve existing classifiers and develop new systems that overcome these limitations necessitates novel methods to crowdsource labeled emotion data from children. In this paper, we present a mobile charades-style game, Guess What?, from which we derive egocentric video with a high density of varied emotion from a 90-second game session. We then present a framework for semi-automatic labeled frame extraction from these videos using meta information from the game session coupled with classification confidence scores. Results show that 94%, 81%, 92%, and 56% of frames were automatically labeled correctly for categories disgust, neutral, surprise, and scared respectively, though performance for angry and happy did not improve significantly from the baseline.
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