Human Emotion Recognition: Review of Sensors and Methods Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or humanxe2x80x93robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skinpenetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

Toward Fear Detection using Affect Recognition Affective Internet of Things (AIoT) uses sensing technology empowered with the capability of detecting or predicting the emotional or affective state of the person. Recently, personal area networks in wearable systems have received increasing attention regarding affect recognition research. The combination of marketready technology and smart integrated sensors makes wearablebased affect recognition an essential building block. In this paper we present an ongoing PhD thesis work toward a fear detection system. A reduced set of wearableready modalities sensors are extracted, including only blood volume pulse and galvanic skin resistance, from publicly available datasets oriented to affect recognition. We evaluate the feasibility of a novel, multimodal approach to recognize the emotional quadrant, based on arousal and valence scores, in which fear and other negative emotions are embedded. The results are compared with the current state of the art and outlines are given for the next steps towards a fearspecific detection system.