Research on Emotion Recognition for Online Learning in a Novel Computing Model

• Published in: Applied sciences Vol. 12; no. 9; p. 4236
• Main Authors: Chen, Mengnan, Xie, Lun, Li, Chiqin, Wang, Zhiliang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2022
• Subjects: Accuracy; Affective computing; Algorithms; Arousal; Artificial intelligence; Classification; Computer architecture; Datasets; Deep learning; Distance learning; electrocardiogram; Electrocardiography; Emotion recognition; Emotions; Feature selection; galvanic skin response; Human-computer interaction; Information processing; Learning; Machine learning; Medical research; Nervous system; Neural networks; online learning; physiological signals; Physiology; Real time; Sensors; Sentiment analysis; terminal-edge-cloud; Wavelet transforms
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12094236

The recognition of human emotions is expected to completely change the mode of human-computer interaction. In emotion recognition research, we need to focus on accuracy and real-time performance in order to apply emotional recognition based on physiological signals to solve practical problems. Considering the timeliness dimension of emotion recognition, we propose a terminal-edge-cloud system architecture. Compared to traditional sentiment computing architectures, the proposed architecture in this paper reduces the average time consumption by 15% when running the same affective computing process. Proposed Joint Mutual Information (JMI) based feature extraction affective computing model, and we conducted extensive experiments on the AMIGOS dataset. Through experimental comparison, this feature extraction network has obvious advantages over the commonly used methods. The model performs sentiment classification, and the average accuracy of valence and arousal is 71% and 81.8%, compared with recent similar sentiment classifier research, the average accuracy is improved by 0.85%. In addition, we set up an experiment with 30 people in an online learning scenario to validate the computing system and algorithm model. The result proved that the accuracy and real-time recognition were satisfactory, and improved the online learning real-time emotional interaction experience.