Multi-physiological signal fusion for objective emotion recognition in educational human–computer interaction
Bibliographic Data
| ID | 22071607 |
|---|---|
| Authors | Wanmeng Wu, Wen‐Yen Wu (0000-0002-8522-7911, Changchun University of Science and Technology), Enling Zuo (Changchun Normal University), Weiya Zhang (0000-0003-1142-1460, Jilin Normal University), Xiangjie Meng (Changchun University) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1492375-1492375 |
| Publication date | 2024-11-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1492375 |
| PMID | 39659721 |
| OpenAlex | W4404728544 |
| Language | EN |
| References cited | 23 |
Introduction: An increasing prevalence of psychological stress and emotional issues among higher education teachers necessitates innovative approaches to promote their wellbeing. Emotion recognition technology, integrated into educational human-computer interaction (HCI) systems, offers a promising solution. This study aimed to develop a robust emotion recognition system to enhance teacher-student interactions within educational HCI settings. Methods: A multi-physiological signal-based emotion recognition system was developed using wearable devices to capture electrocardiography (ECG), electromyography (EMG), electrodermal activity, and respiratory signals. Feature extraction was performed using time-domain and time-frequency domain analysis methods, followed by feature selection to eliminate redundant features. A convolutional neural network (CNN) with attention mechanisms was employed as the decision-making model. Results: The proposed system demonstrated superior accuracy in recognizing emotional states than existing methods. The attention mechanisms provided interpretability by highlighting the most informative physiological features for emotion classification. Discussion: The developed system offers significant advancements in emotion recognition for educational HCI, enabling more accurate and standardized assessments of teacher emotional states. Real-time integration of this technology into educational environments can enhance teacher-student interactions and contribute to improved learning outcomes. Future research can explore the generalizability of this system to diverse populations and educational settings
Emotion recognition · Human–computer interaction · Speech recognition · Computer Science · EEG and Brain-Computer Interfaces · Emotion and Mood Recognition · Mental Health via Writing · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |