LD-Recognition
Classroom Action Recognition Based on Passive RFID
Bibliographic Data
| ID | 22107362 |
|---|---|
| Authors | Qing Qiu (Anhui Normal University), Taochun Wang (0000-0003-4717-9303, Anhui Normal University), Fulong Chen (0000-0003-1144-0004, Anhui Normal University), Chengtian Wang (0009-0005-4624-7273, Anhui Normal University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Pages | 1182-1191 |
| Publication date | 2024-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3234423 |
| OpenAlex | W4315631077 |
| Language | EN |
| References cited | 32 |
Classroom learning is one of the main ways for students to acquire knowledge because the class usually has a large number of students and it is difficult for teachers to pay attention to the learning status of each student at the same time. Therefore, mastering the learning status of each student and dealing with it is the key factor to determine the quality of course teaching, and classroom actions can accurately reflect the learning state of students. Based on this, in this article, a passive radio frequency identification (RFID)-based classroom action recognition system LD-recognition is proposed. The system pastes the label on the right side of the desk, and the learning state of the students was judged by recognizing the four movements of raising the left hand, raising the right hand, nodding off, and holding the book. The system uses a multichannel attentional graph convolutional neural network (ATGCN) to deeply learn the phase and signal strength of actions and conduct action recognition. LD-recognition verifies the accuracy of actions from different distances, different experimenters, and different network models. The experimental results show that the recognition accuracy of LD-recognition system is high, reaching 96.9% on average
Convolutional neural network · Desk · Graph · Computer Science · Engineering · Gaze Tracking and Assistive Technology · Hand Gesture Recognition Systems · Indoor and Outdoor Localization Technologies · Artificial Intelligence
| Highly cited | No |
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