The use of robots had already been proven to encourage the promotion of social interaction and skills lacking in children with Autism Spectrum Disorders (ASD), who typically have difficulties in recognizing facial expressions and emotions. The main goal of this research is to study the influence of a humanoid robot to develop socio-emotional skills in children with ASD. The children’s performance in game scenarios aiming to develop facial expressions recognition skills is presented. Along the sessions, children who performed the game scenarios with the robot and the experimenter had a significantly better performance than the children who performed the game scenarios without the robot. The main conclusions of this research support that a humanoid robot is a useful tool to develop socio-emotional skills in the intervention of children with ASD, due to the engagement and positive learning outcome observed.
Facial expressions play an important role during human social interaction, enabling communicative cues, ascertaining the level of interest or signalling the desire to take a speaking turn. They also give continuous feedback indicating that the information conveyed has been understood. However, certain individuals have difficulties in social interaction in particular verbal and non-verbal communication (e.g. emotions and gestures). Autism Spectrum Disorders (ASD) are a special case of social impairments. Individuals that are affected with ASD are characterized by repetitive patterns of behaviour, restricted activities or interests, and impairments in social communication. The use of robots had already been proven to encourage the promotion of social interaction and skills in children with ASD. Following this trend, in this work a robotic platform is used as a mediator in the social interaction activities with children with special needs. The main purpose of this dissertation is to develop a system capable of automatic detecting emotions through facial expressions and interfacing it with a robotic platform in order to allow social interaction with children with special needs. The proposed experimental setup uses the Intel RealSense 3D camera and the Zeno R50 Robokind robotic platform. This layout has two subsystems, a Mirroring Emotion System (MES) and an Emotion Recognition System (ERS). The first subsystem (MES) is capable of synthetizing human emotions through facial expressions, on-line. The other subsystem (ERS) is able to recognize human emotions through facial features in real time. MES extracts the user facial Action Units (AUs), sends the data to the robot allowing on-line imitation. ERS uses Support Vector Machine (SVM) technique to automatic classify the emotion expressed by the User in real time. Finally, the proposed subsystems, MES and ERS, were evaluated in a laboratorial and controlled environment in order to check the integration and operation of the systems. Then, both subsystems were tested in a school environment in different configurations. The results of these preliminary tests allowed to detect some constraints of the system, as well as validate its adequacy in an intervention setting.
Facial expressions are of utmost importance in social interactions, allowing communicative prompts for a speaking turn and feedback. Nevertheless, not all have the ability to express themselves socially and emotionally in verbal and non-verbal communication. In particular, individuals with Autism Spectrum Disorder (ASD) are characterized by impairments in social communication, repetitive patterns of behaviour, and restricted activities or interests. In the literature, the use of robotic tools is reported to promote social interaction with children with ASD. The main goal of this work is to develop a system capable of automatic detecting emotions through facial expressions and interfacing them with a robotic platform (Zeno R50 Robokind® robotic platform, named ZECA) in order to allow social interaction with children with ASD. ZECA was used as a mediator in social communication activities. The experimental setup and methodology for a real-time facial expression (happiness, sadness, anger, surprise, fear, and neutral) recognition system was based on the Intel® RealSense™ 3D sensor and on facial features extraction and multiclass Support Vector Machine classifier. The results obtained allowed to infer that the proposed system is adequate in support sessions with children with ASD, giving a strong indication that it may be used in fostering emotion recognition and imitation skills.
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