Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Mobile Networks and Applications
A pervasive computing system for the operating room of the future
Mobile Networks and Applications
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
On-line recognition of surgical activity for monitoring in the operating room
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Computers in Biology and Medicine
Phase recognition during surgical procedures using embedded and body-worn sensors
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Journal of Biomedical Informatics
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We present a system that recognizes human activities during trauma resuscitation, the fast-paced and team-based initial management of injured patients in the emergency department. Most objects used in trauma resuscitation are uniquely associated with tasks. To detect object use, we employed passive radio frequency identification (RFID) for their size and cost advantages. We designed the system setup to ensure the effectiveness of passive tags in such a complex setting, which includes various objects and significant human motion. Through our studies conducted at a Level 1 trauma center, we learned that objects used in trauma resuscitation need to be tagged differently because of their size, shape, and material composition. Based on this insight, we classified the medical items into groups based on usage and other characteristics. Objects in different groups are tagged differently and their data is processed differently. We applied machine-learning algorithms to identify object-state changes and process the RFID data using algorithms specific to object groups. Our results show that RFID has significant potential for automatic detection of object usage in complex and fast-paced settings.