| Literature DB >> 30370332 |
Xinyu Li1, Dongyang Yao1, Xuechao Pan1, Jonathan Johannaman1, JaeWon Yang2, Rachel Webman2, Aleksandra Sarcevic3, Ivan Marsic1, Randall S Burd2.
Abstract
We describe a novel and practical activity recognition system for dynamic and complex medical settings using only passive RFID technology. Our activity recognition approach is based on the use of objects that are specific for a given activity. The object-use status is detected from RFID data and the activities are predicted from the statuses of use of different objects. We tagged 10 objects in a trauma room of an emergency department and recorded RFID data for 10 actual trauma resuscitations. More than 20,000 seconds of data were collected and used for analysis. The system achieved a 96% overall accuracy with a 0.74 F-score for detecting use of 10 common resuscitation objects and 95% accuracy with a 0.30 F Score for activity recognition of 10 medical activities.Entities:
Keywords: activity recognition; machine learning; object-use detection; passive RFID; tagging strategies
Year: 2016 PMID: 30370332 PMCID: PMC6200354 DOI: 10.1109/RFID.2016.7488002
Source DB: PubMed Journal: IEEE Int Conf RFID ISSN: 2374-0221