A Mobile Game for Automatic Emotion-Labeling of Images.

In this paper, we describe challenges in the development of a mobile charades-style game for delivery of social training to children with Autism Spectrum Disorder (ASD). Providing real-time feedback and adapting game difficulty in response to the child's performance necessitates the integration of emotion classifiers into the system. Due to the limited performance of existing emotion recognition platforms for children with ASD, we propose a novel technique to automatically extract emotion-labeled frames from video acquired from game sessions, which we hypothesize can be used to train new emotion classifiers to overcome these limitations.

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study.

Background: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. The incidence of ASD has increased in recent years; it is now estimated that approximately 1 in 40 children in the United States are affected. Due in part to increasing prevalence, access to treatment has become constrained.

Labeling images with facial emotion and the potential for pediatric healthcare.

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.