Name
Abstract | ||
|---|---|---|
We evaluated passive radio-frequency identification (RFID)technology for detecting the use of objects and related activities during trauma resuscitation. Our system consisted of RFID tags and antennas, optimally placed for object detection, as well as algorithms for processing the RFID data to infer object use. To evaluate our approach, we tagged 81 objects in the resuscitation room and recorded RFID signal strength during 32 simulated resuscitations performed by trauma teams. We then analyzed RFID data to identify cues for recognizing resuscitation activities. Using these cues, we extracted descriptive features and applied machine-learning techniques to monitor interactions with objects. Our results show that an instance of a used object can be detected with accuracy rates greater than 90 percentin a crowded and fast-paced medical setting using off-the-shelf RFID equipment, and the time and duration of use can be identified with up to 83 percent accuracy. Our results also offer insights into the limitations of passive RFID and areas in which it needs to be complemented with other sensing technologies. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TMC.2015.2436393 | IEEE Trans. Mob. Comput. |
Keywords | Field | DocType |
Antennas,Passive RFID tags,Fluids,Feature extraction,Mobile computing | Mobile computing,Object detection,Activity recognition,Computer science,Trauma resuscitation,Real-time computing,Feature extraction,Speech recognition,Signal strength,Resuscitation room,Distributed computing | Journal |
Volume | Issue | ISSN |
PP | 99 | 1536-1233 |
Citations | PageRank | References |
4 | 0.42 | 22 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siddika Parlak | 1 | 113 | 6.82 |
| Ivan Marsic | 2 | 716 | 91.96 |
| Aleksandra Sarcevic | 3 | 182 | 26.75 |
| Waheed Uz Zaman Bajwa | 4 | 106 | 8.00 |
| Lauren J Waterhouse | 5 | 6 | 0.79 |
| Randall S. Burd | 6 | 122 | 21.53 |