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. Our technique, which uses probability scores from three different classifiers and meta information from game sessions, correctly identified 83% of frames compared to a baseline of 51.6% from the best emotion classification API evaluated in our work.

Examining games from a fresh perspective we present the idea of game-inspired and game-based algorithms, dubbed .