Huiping Jiang^1,*, Rui Jiao¹, Demeng Wu¹, Wenbo Wu²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2315-2327, 2021, DOI:10.32604/cmc.2021.016832

Abstract With the rapid development of deep learning and artificial intelligence, affective computing, as a branch field, has attracted increasing research attention. Human emotions are diverse and are directly expressed via non-physiological indicators, such as electroencephalogram (EEG) signals. However, whether emotion-based or EEG-based, these remain single-modes of emotion recognition. Multi-mode fusion emotion recognition can improve accuracy by utilizing feature diversity and correlation. Therefore, three different models have been established: the single-mode-based EEG-long and short-term memory (LSTM) model, the Facial-LSTM model based on facial expressions processing EEG data, and the multi-mode LSTM-convolutional neural network (CNN) model that combines expressions and EEG. Their… More >

Jinseok Woo, János Botzheim, Naoyuki Kubota

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 829-841, 2018, DOI:10.1080/10798587.2017.1364919

Abstract This paper introduces the integrated system of a smart-device-based cognitive robot partner called iPhonoid-C. Interaction with a robot partner requires many elements, including verbal communication, nonverbal communication, and embodiment as well. A robot partner should be able to understand human sentences, as well as nonverbal information such as human gestures. In the proposed system, the robot has an emotional model connecting the input information from the human with the robot’s behavior. Since emotions are involved in human natural communication, and emotion has a significant impact on humans’ actions, it is important to develop an emotional model for the robot partner… More >