Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and humancomputer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A xe2x80x98neutral  targetxe2x80x99 pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from Chinaxe2x80x99s standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of xe2x80x98neutral  targetxe2x80x99 video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods.

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.