Facilitating the Child–Robot Interaction by Endowing the Robot with the Capability of Understanding the Child Engagement: The Case of Mio Amico Robot

AbstractSocial Robots (SRs) are substantially becoming part of modern society, given their frequent use in many areas of application including education, communication, assistance, and entertainment. The main challenge in human–robot interaction is in achieving human-like and affective interaction between the two groups. This study is aimed at endowing SRs with the capability of assessing the emotional state of the interlocutor, by analyzing his/her psychophysiological signals. The methodology is focused on remote evaluations of the subject's peripheral neuro-vegetative activity by means of thermal infrared imaging. The approach was developed and tested for a particularly challenging use case: the interaction between children and a commercial educational robot, Mio Amico Robot, produced by LiscianiGiochi©. The emotional state classified from the thermal signal analysis was compared to the emotional state recognized by a facial action coding system. The proposed approach was reliable and accurate and favored a personalized and improved interaction of children with SRs

Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%