Activity recognition for medical teamwork based on passive RFID

Xinyu Li, Dongyang Yao, Xuechao Pan, Jonathan Johannaman, Jaewon Yang, Rachel Webman, Aleksandra Sarcevic, Ivan Marsic, Randall S. Burd

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on RFID, RFID 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467388078
DOIs
StatePublished - Jun 8 2016
Event2016 IEEE International Conference on RFID, RFID 2016 - Orlando, United States
Duration: May 2 2016May 5 2016

Publication series

Name2016 IEEE International Conference on RFID, RFID 2016

Other

Other2016 IEEE International Conference on RFID, RFID 2016
Country/TerritoryUnited States
CityOrlando
Period5/2/165/5/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Instrumentation

Keywords

  • activity recognition
  • machine learning
  • object-use detection
  • passive RFID
  • tagging strategies

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