Physiological sensor system for the detection of human moods towards internet of robotic things applications

Internet of Robotic Things paradigm offers a concrete support to daily life. The pervasiveness of smart things, together with advances in cloud robotics, can help the smart systems to perceive and collect more information about the users and the environment. Often citizens have experienced “one-size-fits-all” approach, since the delivered service was not personalized, therefore resulting far from user’s expectations. Hence, future smart agents, like robots, should produce personalized behaviours based on user emotions and moods in order to be more integrated into ordinary activities. In this work, we investigated the performances with unsupervised and supervised approaches to recognize three different moods elicited during a social interaction by means of a wearable system capable of measuring the Electrocardiogram, the ElectroDermal Activity and the Electroencephalographic signals. Particularly, the classification problem was analysed using three unsupervised (K-Mean, Self-Organizing Map and Hierarchical Clustering) and three supervised methods (Support Vector Machine, Decision Tree and k-nearest neighbour). The supervised algorithms reached an accuracy of 0.86 in the best case. The outcomes show that even in an unsupervised context the system is able to recognize the mood, reaching an accuracy equal to 0.76 in the best case.