Name
Abstract | ||
|---|---|---|
We propose a Deep Neural Network (DNN) structure for RFID-based activity recognition. RFID data collected from several reader antennas with overlapping coverage have potential spatiotemporal relationships that can be used for object tracking. We augmented the standard fully-connected DNN structure with additional pooling layers to extract the most representative features. For model training and testing, we used RFID data from 12 tagged objects collected during 25 actual trauma resuscitations. Our results showed 76% recognition micro-accuracy for 7 resuscitation activities and 85% average micro-accuracy for 5 resuscitation phases, which is similar to existing system that, however, require the user to wear an RFID antenna. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2987354.2987355 | S3@MobiCom |
Keywords | DocType | Volume |
Activity Recognition,Deep Neural Network,Max Pooling,RFID | Conference | 2016 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Li | 1 | 88 | 37.72 |
| Yanyi Zhang | 2 | 29 | 6.40 |
| Mengzhu Li | 3 | 0 | 0.68 |
| Ivan Marsic | 4 | 716 | 91.96 |
| JaeWon Yang | 5 | 10 | 1.35 |
| Randall S. Burd | 6 | 122 | 21.53 |