ABSTRACT
Computational models of emotions can not only improve the effectiveness and efficiency of human-robot interaction but also coordinate a robot to adapt to its environment better. When designing computational models of emotions for socially interactive robots, especially for robots for people with special needs such as autistic children, one should take into account the social and communicative characteristics of such groups of people. This article presents a novel computational model of emotions called AppraisalCloudPCT that is suitable for socially interactive robots that can be adopted in autistic rehabilitation which, to the best of our knowledge, is the first computational model of emotions built for robots that can satisfy the needs of a special group of people such as autistic children. To begin with, some fundamental and notable computational models of emotions (e.g., OCC, Scherer's appraisal theory, PAD) that have deep and profound influence on building some significant models (e.g., PRESENCE, iGrace, xEmotion) for socially interactive robots are revisited. Then, a comparative assessment between our AppraisalCloudPCT and other five significant models for socially interactive robots is conducted. Great efforts have been made in building our proposed model to meet all of the six criteria for comparison, by adopting the appraisal theories on emotions, perceptual control theory on emotions, a component model view of appraisal models, and cloud robotics. Details of how to implement our model in a socially interactive robot we developed for autistic rehabilitation are also elaborated in this article. Future studies should examine how our model performs in different robots and also in more interactive scenarios.
Subject(s)
Autistic Disorder , Robotics , Child , Humans , Emotions , Communication , Computer SimulationABSTRACT
Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we propose a novel framework for human-computer/robot interaction and introduce a preliminary intervention study for improving the emotion recognition of Chinese children with an autism spectrum disorder. The core of the framework is the Facial Emotion Cognition and Training System (FECTS, including six tasks to train children with ASD to match, infer, and imitate the facial expressions of happiness, sadness, fear, and anger) based on Simon Baron-Cohen's E-S (empathizing-systemizing) theory. Our system may be implemented on PCs, smartphones, mobile devices such as PADs, and robots. The training record (e.g., a tracked record of emotion imitation) of the Chinese autistic children interacting with the device implemented using our FECTS will be uploaded and stored in the database of a cloud-based evaluation system. Therapists and parents can access the analysis of the emotion learning progress of these autistic children using the cloud-based evaluation system. Deep-learning algorithms of facial expressions recognition and attention analysis will be deployed in the back end (e.g., devices such as a PC, a robotic system, or a cloud system) implementing our FECTS, which can perform real-time tracking of the imitation quality and attention of the autistic children during the expression imitation phase. In this preliminary clinical study, a total of 10 Chinese autistic children aged 3-8 are recruited, and each of them received a single 20-minute training session every day for four consecutive days. Our preliminary results validated the feasibility of the developed FECTS and the effectiveness of our algorithms based on Chinese children with an autism spectrum disorder. To verify that our FECTS can be further adapted to children from other countries, children with different cultural/sociological/linguistic contexts should be recruited in future studies.
