Leveraging video data from a digital smartphone autism therapy to train an emotion detection classifier

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting one in 40 children in the United States and is associated with impaired social interactions, restricted interests, and repetitive behaviors. Previous studies have demonstrated the promise of applying mobile systems with real-time emotion recognition to autism therapy, but existing platforms have shown limited performance on videos of children with ASD. We propose the development of a new emotion classifier designed specifically for pediatric populations, trained with images crowdsourced from an educational mobile charades-style game: Guess What?. We crowdsourced the acquisition of videos of children portraying emotions during remote game sessions of Guess What? that yielded 6,344 frames from fifteen subjects. Two raters manually labeled the frames with four of the Ekman universal emotions (happy, scared, angry, sad), a neutral class, and n/a for frames with an indeterminable label. The data were pre-processed, and a model was trained with a transfer-learning and neural-architecture-search approach using the Google Cloud AutoML Vision API. The resulting classifier was evaluated against existing approaches (Azure Face API from Microsoft and Rekognition from Amazon Web Services) using the standard metrics of F1 score. The resulting classifier demonstrated superior performance across all evaluated emotions, supporting our hypothesis that a model trained with a pediatric dataset would outperform existing emotion-recognition approaches for the population of interest. These results suggest a new strategy to develop precision therapy for autism at home by integrating the model trained with a personalized dataset to the mobile game.