Automatic Emotion Recognition in Robot-Children Interaction for ASD Treatment

Abstract

Autism Spectrum Disorders (ASD) are a group of lifelong disabilities that affect people's communication and understanding social cues. The state of the art witnesses how technology, and in particular robotics, may offer promising tools to strengthen the research and therapy of ASD. This work represents the first attempt to use machine-learning strategies during robot-ASD children interactions, in terms of facial expression imitation, making possible an objective evaluation of children's behaviours and then giving the possibility to introduce a metric about the effectiveness of the therapy. In particular, the work focuses on the basic emotion recognition skills. In addition to the aforementioned applicative innovations this work contributes also to introduce a facial expression recognition (FER) engine that automatically detects and tracks the child's face and then recognize emotions on the basis of a machine learning pipeline based on HOG descriptor and Support Vector Machines. Two different experimental sessions were carried out: the first one tested the FER engine on publicly available datasets demonstrating that the proposed pipeline outperforms the existing strategies in terms of recognition accuracy. The second one involved ASD children and it was a preliminary exploration of how the introduction of the FER engine in the therapeutic protocol can be effectively used to monitor children's behaviours.