Name
Abstract | ||
|---|---|---|
As the basis of human-computer interaction (HCI), gesture recognition interprets user-performed gestures as commands, followed by the content execution expressed by users' gestures. Gesture recognition through wireless signals denotes a novel branch of human perception. Despite the recent popularity of Radio Frequency Identification (RFID) following specific advantages (lightweight, low-cost, and universality), several intricacies remain unresolved in RFID sensing research. First, most studies performed simplified body movements assessments instead of identifying complex and fine-grained or subtle gestures. Second, users require extensive training in a novel discipline to collect training data in a specific pattern. Given the paucity of an intuitive and effective means of identifying user gestures, the RF-E-letter proposed in this study denotes an RFID recognition system for complex, fine-grained, and domain-independent gestures. A multi-label array was utilized to gather gesture signals. Fine-grained gesture data could be obtained pre-processing with a novel data-processing method. Seemingly irregular RFID phase data could be converted into intuitive images for the deep learning module input as convolutional neural networks (CNNs) encompass automatic extraction characteristics for complex space-time features. The average accuracy of new environments for novel users is 95.6% and 96.6%, respectively (significantly better than current RFID-based solutions), thus demonstrating effectiveness and versatility. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3128293 | IEEE ACCESS |
Keywords | DocType | Volume |
Gesture recognition, Feature extraction, Sensors, Wireless sensor networks, Wireless communication, Target tracking, Radar tracking, Gesture recognition, RFID, tags | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhixiong Yang | 1 | 0 | 0.34 |
| X.L. Liu | 2 | 11 | 11.83 |
| Zijian Li | 3 | 0 | 0.34 |
| Bo Yuan | 4 | 0 | 0.34 |
| Yajun Zhang | 5 | 2 | 1.07 |