Emo-mirror: a proposal to support emotion recognition in children with autism spectrum disorders

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder defined as persistent difficulty in maturing the socialization process. Health professionals have used traditional methods in the therapies performed on patients with the aim of improving the expression of emotions by patients. However, they have not been sufficient to detect the different emotions expressed in the face of people according to different sensations. Therefore, different artificial intelligence techniques have been applied to improve the results obtained in these therapies. In this article, we propose the construction of an intelligent mirror to recognize five basic emotions: angry, scared, sad, happy and neutral. This mirror uses convolutional neural networks to analyze the images that are captured by a camera and compare it with the one that the patient should perform, thus supporting the therapies performed by health professionals in children with ASD. The proposal presents the platform and computer architecture, as well as the evaluation by specialists under the technology acceptance model.