Research on Emotion Recognition for Online Learning in a Novel Computing Model
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. Consi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/9/4236 |
| _version_ | 1797505762892709888 |
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| author | Mengnan Chen Lun Xie Chiqin Li Zhiliang Wang |
| author_facet | Mengnan Chen Lun Xie Chiqin Li Zhiliang Wang |
| author_sort | Mengnan Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:22:52Z |
| format | Article |
| id | doaj.art-6d45f158c9c04873acf2b9f07422bb27 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:52Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6d45f158c9c04873acf2b9f07422bb272023-11-23T07:46:10ZengMDPI AGApplied Sciences2076-34172022-04-01129423610.3390/app12094236Research on Emotion Recognition for Online Learning in a Novel Computing ModelMengnan Chen0Lun Xie1Chiqin Li2Zhiliang Wang3School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe 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.https://www.mdpi.com/2076-3417/12/9/4236physiological signalsaffective computingonline learninggalvanic skin responseelectrocardiogramterminal-edge-cloud |
| spellingShingle | Mengnan Chen Lun Xie Chiqin Li Zhiliang Wang Research on Emotion Recognition for Online Learning in a Novel Computing Model Applied Sciences physiological signals affective computing online learning galvanic skin response electrocardiogram terminal-edge-cloud |
| title | Research on Emotion Recognition for Online Learning in a Novel Computing Model |
| title_full | Research on Emotion Recognition for Online Learning in a Novel Computing Model |
| title_fullStr | Research on Emotion Recognition for Online Learning in a Novel Computing Model |
| title_full_unstemmed | Research on Emotion Recognition for Online Learning in a Novel Computing Model |
| title_short | Research on Emotion Recognition for Online Learning in a Novel Computing Model |
| title_sort | research on emotion recognition for online learning in a novel computing model |
| topic | physiological signals affective computing online learning galvanic skin response electrocardiogram terminal-edge-cloud |
| url | https://www.mdpi.com/2076-3417/12/9/4236 |
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