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We describe the process of introducing RFID technology in the trauma bay of a trauma center to support fast-paced and complex teamwork during resuscitation. We analyzed trauma resuscitation tasks, photographs of medical tools, and videos of simulated resuscitations to gain insight into resuscitation tasks, work practices and procedures. Based on these data, we discuss strategies for placing RFID tags on medical tools and for placing antennas in the environment for optimal tracking and activity recognition. Results from our preliminary RFID deployment in the trauma bay show the feasibility of our approach for tracking tools and for recognizing trauma team activities. We conclude by discussing implications for and challenges to introducing RFID technology in other similar settings characterized by dynamic and collocated collaboration.

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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 percent in 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.

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.